Protein arginylation mediated by arginyltransferase 1 is a crucial regulator of cellular processes in eukaryotes by affecting protein stability, function, and interaction with other macromolecules. This enzyme and its targets are of immense interest for modulating cellular processes in diseased states like obesity and cancer. Despite being an important target molecule, no highly potent drug against this enzyme exists. Therefore, this study focuses on discovering potential inhibitors of human arginyltransferase 1 by computational approaches where screening of over 300,000 compounds from natural and synthetic databases was done using a pharmacophore model based on common features among known inhibitors. The drug-like properties and potential toxicity of the compounds were also assessed in the study to ensure safety and effectiveness. Advanced methods, including molecular simulations and binding free energy calculations, were performed to evaluate the stability and binding efficacy of the most promising candidates. Ultimately, three compounds were identified as potent inhibitors, offering new avenues for developing therapies targeting arginyltransferase 1.
Keywords: ATE1; Arginyltransferase 1; Ki prediction; human ATE1 inhibitor; machine learning; pharmacophore.