In-silico molecular prediction of de-novo pteridophytic ligands targeting fungal Sec-14p: A CADD based analysis

Abstract

Applications of Computer-Aided Drug Design (CADD) tools in the healthcare system are a developing area of research. Implementation of in-silico, structure-based virtual screening techniques in drug discovery to identify potential agonists or inhibitors based upon the binding affinity towards corresponding target proteins is a widely used technique in drug discovery. This advanced technique is used in this study to identify potential candidates from Himalayan pteridophytes possessing antifungal potentials through inhibition of Sec14p, a phosphatidylinositol/phosphatidylcholine transfer protein from Saccharomyces cerevisiae that is complexed with Picolinamide. 3D conformations for the chemical structures of identified phytoconstituents were prepared through Chem Draw 16.0 program for prediction of activity (way2drug), mechanism (PyRx), physicochemical & pharmacokinetics (SWISS-ADME) and toxicological (PROTOX-2) probabilities through in-silico approaches for all selected 180 ligands. BIOVIA discovery studio visualizer was used to generate a 3D and 2D image of the most active 19 among the 180 identified ligands. Comparative studies proposed some highly potential effective and safe compounds from Himalayan pteridophytes that could interfere with fungal cell growth.