De novo design of caseinolytic protein proteases inhibitors based on pharmacophore and 2D molecular fingerprints.

Abstract Caseinolytic protein proteases (ClpP) are large oligomeric protein complexes that contribute to cell homeostasis as well as virulence regulation in bacteria. Inhibitors of ClpP can significantly attenuate the capability to produce virulence factors of the bacteria. In this work, we developed a workflow to expand the chemical space of potential ClpP inhibitors based on a set of β-lactones. In our workflow, an artificial pharmacophore model was generated based on HipHop and HYPOGEN method. A de novo compound library based on molecular fingerprints was constructed and virtually screened by the pharmacophore model. The results were further investigated by molecular docking study. The workflow successfully achieved potential ClpP inhibitors. It could be applied to design more novel potential ClpP inhibitors and provide theoretical basis for the further optimization of the hit compounds.