Wu Guanzhong, Zhang Zhen, Chen Hong, Lin Kejiang
School of Pharmacy, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China.
School of Pharmacy, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China.
Bioorg Med Chem Lett. 2015 Jun 1;25(11):2345-52. doi: 10.1016/j.bmcl.2015.04.035. Epub 2015 Apr 17.
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.
酪蛋白水解蛋白酶（ClpP）是大型寡聚蛋白复合物，有助于细菌的细胞稳态以及毒力调节。ClpP抑制剂可显著减弱细菌产生毒力因子的能力。在这项工作中，我们基于一组β-内酯开发了一种工作流程，以扩展潜在ClpP抑制剂的化学空间。在我们的工作流程中，基于HipHop和HYPOGEN方法生成了一个人工药效团模型。构建了基于分子指纹的全新化合物库，并通过药效团模型进行虚拟筛选。通过分子对接研究进一步研究结果。该工作流程成功获得了潜在的ClpP抑制剂。它可用于设计更多新型潜在ClpP抑制剂，并为命中化合物的进一步优化提供理论依据。
