¹Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Hacettepe University, Sıhhiye 06100 Ankara, Turkey
²Leicester School of Allied Health Sciences, Faculty of Health and Life Sciences, De Montfort University, Leicester LE1 9BH, United Kingdom DOI : 10.29228/jrp.159 Secretory aspartate proteases (SAPs) is a key virulence factor of Candida spp. enabling adherence to and invasion of host tissues through breakdown of host proteins related to immunological and structural defenses, making them potential drug targets for drug-resistant mycoses, especially where the available therapies fail. To date, no SAP inhibitors for Candida tropicalis, one of the top five most common species isolated in Candida-related mycoses, has been reported. In this study, we report first-time identification of a set of potential C. tropicalis SAP1 (SAPT1) inhibitors through a hybrid ligand-and structure-based virtual screening of National Cancer Institute (NCI) library compounds. The NCI library was refined by filtering off non-druglike molecules. Referring to a known C. albicans SAP inhibitor, a similarity search was performed for the refined library, in addition to a pharmacophore screen using a model of the ligand-receptor interactions between a peptide substrate and SAPT1 obtained from crystallographic data. The compounds selected from these screens were subject to molecular docking to the SAPT1 active site and the top-scoring ligand-receptor complexes were further included in MM-GBSA calculations to optimize the predicted binding affinities. Finally, the selected 16 compounds, which were confirmed to make key interactions with the catalytic residues, were in silico evaluated and found eligible for certain pharmacokinetic properties. As a future prospect, obtaining these virtual hits and testing them in vitro against SAPT1 could validate the virtual screening process and yield the first smallmolecule inhibitors of SAPT. Keywords : secretory aspartate protease; Candida tropicalis; virtual screening; shape screening; pharmacophore modeling; molecular docking; MM-GBSA