In-silico identification of novel Cis-aconitate decarboxylase inhibitors as potential anti-inflammatory agents using molecular docking and dynamics

Cis-aconitate decarboxylase (CAD), also known as ACOD1 or IRG1, catalyzes the conversion of cis-aconitate to itaconate, playing a pivotal role in innate immunity and inflammatory diseases. Although CAD is a key enzyme in the pathophysiology of inflammatory diseases, no specific inhibitors for CAD currently exist. In this study, we screened 86,326 compounds similar to CAD ligand from the Zinc database to identify potential CAD inhibitors using molecular docking with Molegro Virtual Docker and AutoDock-Vina, followed by molecular dynamics (MD) simulations. The top candidates were further assessed through molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and free energy estimation using the MM/GBSA method. Pharmacokinetics, drug-likeness, and toxicity were evaluated using SWISSADME and Discovery Studio. Among the tested ligands, four compounds demonstrated strong binding affinity, stable interactions, and favorable pharmacokinetic properties, including high gastrointestinal absorption, solubility, and non-toxicity. These compounds demonstrated promising pharmacokinetics, good gastrointestinal absorption, solubility, non-toxicity, and compliance with Lipinski's rules for drug-like properties. MD simulations further confirmed the stability of ligand-CAD complexes, with cumulative deviations and fluctuations under 2 Å. These findings suggest novel CAD inhibitors with potential as anti-inflammatory agents, paving the way for CAD-targeted drug discovery.