Investigation of Some Phenolic Compounds as iNOS Inhibitors: An in silico Approach

Nitric oxide synthase (NOS) preferentially produces its inducible isoform (iNOS) in several cells, including macrophages, which are in charge of the immunological response. It is widely known that the expression of iNOS and the consequent production of nitric oxide (NO) have various biological benefits, such as anti-inflammatory and antioxidant properties. However, the overexpression or dysregulation of iNOS can result in the excessive release of NO, which may contribute to the pathogenesis of several disorders. Examining how bioactive plant components interact with iNOS may yield comprehensive and conclusive details on the processes of inhibition. The objective of this study was to employ molecular docking techniques for the investigation of molecular interactions between the iNOS enzyme and specific bioactive compounds, namely resveratrol, 6-gingerol, quercetin, and hydroxytyrosol. The Protein Data Bank (PDB) was the source of the crystal structure for the target protein, iNOS. In contrast, the three-dimensional (3D) structures of the bioactive compounds were acquired from the PubChem database. AutoDock Vina was employed for docking simulations of iNOS inhibitor compounds. The findings from the molecular docking analysis indicated that each of the chosen bioactive substances demonstrated inhibitory effects on iNOS. Furthermore, these compounds consistently formed interactions with the primary protease at the same binding site across all docking studies. The obtained results suggest that quercetin, among other phenolic compounds, could be a potential alternative for iNOS inhibition in a therapeutic context. Nevertheless, computer simulations represent the initial stage in the development of inhibitory compounds, and further research and clinical applications are essential.

Investigation of Some Phenolic Compounds as iNOS Inhibitors: An in silico Approach Keywords:

phenolics , iNOS inhibition , Molecular docking , Inflammation , Computational Biology