Potential inhibitors of methionine aminopeptidase type II identified via structure-based pharmacophore modeling

Abstract

Methionine aminopeptidase (MetAP2) is a metal-containing enzyme that removes initiator methionine from the N-terminus of a newly synthesized protein. Inhibition of the enzyme is crucial in diminishing cancer growth and metastasis. Fumagillin-a natural irreversible inhibitor of MetAP2-and its derivatives are used as potent MetAP2 inhibitors. However, because of their adverse effects, none of them has progressed to clinical studies. In search for potential reversible inhibitors, we built structure-based pharmacophore models using the crystal structure of MetAP2 complexed with fumagillin (PDB ID: 1BOA). The pharmacophore models were validated using Gunner-Henry scoring method. The best pharmacophore consisting of 1 H-bond donor, 1 H-bond acceptor, and 3 hydrophobic features was used to conduct pharmacophore-based virtual screening of ZINC15 database against MetAP2. The top 10 compounds with pharmacophore fit values > 3.00 were selected for further analysis. These compounds were subjected to absorption, distribution, metabolism, elimination, and toxicity (ADMET) prediction and found to have druglike properties. Furthermore, molecular docking calculations was performed on these hits using AutoDock4 to predict their binding mode and binding energy. Three diverse compounds: ZINC000014903160, ZINC000040174591, and ZINC000409110720 with respective binding energy/docking scores of - 9.22, - 9.21, and -817 kcal/mol, were submitted to 100 ns (MD) simulations using Nanoscale MD (NAMD) software. The compounds showed stable binding mode over time. Therefore, they may serve as a scaffold for further computational and experimental optimization toward the design of more potent and safer MetAP2 inhibitors.

Keywords: ADMET prediction; Docking; MD simulation; MetAP2; MetAP2 inhibitors; Structure-based pharmacophore modeling.