Design of Potential Aminopeptidase N (APN) inhibitors via Pharmacophore and Docking-Based Virtual Screening

Introduction: M۱ family of metalloamineopeptidases. The enzyme aminopeptidase N regulates various ofcellular Aminopeptidase N (APN), also known as CD۱۳, is a metal-dependent membrane protease belongingto the functions by different mechanisms, such as peptide breakdown . Aminopeptidase N has a significanteffect on various cellular pathways, including migration, invasion, angiogenesis, and tumor cell metastasis.Overexpression of APN with many diseases such as inflammation, viral infection and especially cancer.Since the enzyme aminopeptidase N plays a crucial role in cancer cell metastasis, it is now considered anattractive target for designing of anticancer drugs. Drug design using computers today is dramaticallyincreasing due to its advantages such as low cost, high speed, and good accuracy. By using informatics, it ispossible to identify new effective and robust compounds. In this study, a hybrid strategy including dockingand virtual screening was used to identify new APN inhibitors.Method: The crystal structure of porcine aminopeptidase-N complexed with bestatin, with the PDB ID of۴fkk, was obtained from Protein Data Bank (www.rcsb.org). A proper pharmacophore model was generatedusing Ligand Scout ۳.۱۲ on the most critical area on the APN active site. Then ZINC libraries (over ۳۵million compounds) were applied for virtual screening.Results and Discussion: Collected compounds from virtual screening were followed by molecular dockingstudies. Seven compounds were selected based on docking score and complying with Lipinski’s “rule offive”. Selected compounds can be considered as a proper candidate in order to develop new APN inhibitors.

Keywords:

Virtual Screening , Molecular Docking , Aminopeptidase N , Inhibitor