Laboratory of Pharmacology-Toxicology, Faculty of Medicine and Pharmacy of Casablanca, Hassan II University of Casablanca, Casablanca, Morocco.
Curr Drug Discov Technol. 2023;20(4):e160423215830. doi: 10.2174/1570163820666230416152843.
The prenyl-binding protein, phosphodiesterase-δ (PDEδ), is essential for the localization of prenylated KRas to the plasma membrane for its signaling in cancer.
The general objective of this work was to develop virtually new potential inhibitors of the PDEδ protein that prevent Ras enrichment at the plasma membrane.
All computational molecular modeling studies were performed by Molecular Operating Environment (MOE). In this study, sixteen crystal structures of PDEδ in complex with fifteen different fragment inhibitors were used in the protein-ligand interaction fingerprints (PLIF) study to identify the chemical features responsible for the inhibition of the PDEδ protein. Based on these chemical characteristics, a pharmacophore with representative characteristics was obtained for screening the BindingDB database. Compounds that matched the pharmacophore model were filtered by the Lipinski filter. The ADMET properties of the compounds that passed the Lipinski filter were predicted by the Swiss ADME webserver and by the ProTox-II-Prediction of Toxicity of Chemicals web server. The selected compounds were subjected to a molecular docking study.
In the PLIF study, it was shown that the fifteen inhibitors formed interactions with residues Met20, Trp32, Ile53, Cys56, Lys57, Arg61, Gln78, Val80, Glu88, Ile109, Ala11, Met117, Met118, Ile129, Thr131, and Tyr149 of the prenyl-binding pocket of PDEδ. Based on these chemical features, a pharmacophore with representative characteristics was composed of three bond acceptors, two hydrophobic elements, and one hydrogen bond donor. When the pharmacophore model was used in the virtual screening of the Binding DB database, 2532 compounds were selected. Then, the 2532 compounds were screened by the Lipinski rule filter. Among the 2532 compounds, two compounds met the Lipinski's rule. Subsequently, a comparison of the ADMET properties and the drug properties of the two compounds was performed. Finally, compound 2 was selected for molecular docking analysis and as a potential inhibitor against PDEδ.
The hit found by the combination of structure-based pharmacophore generation, pharmacophore- based virtual screening, and molecular docking showed interaction with key amino acids in the hydrophobic pocket of PDEδ, leading to the discovery of a novel scaffold as a potential inhibitor of PDEδ.
prenyl-结合蛋白，磷酸二酯酶-δ（PDEδ）对于 Ras 的prenylation 定位到质膜以进行信号转导至关重要。
本工作的总体目标是开发几乎全新的潜在 PDEδ 蛋白抑制剂，以防止 Ras 在质膜上的富集。
所有计算分子建模研究均由分子操作环境（MOE）进行。在这项研究中，使用了十六个 PDEδ 与十五个不同片段抑制剂复合物的晶体结构，用于蛋白-配体相互作用指纹（PLIF）研究，以确定负责 PDEδ 蛋白抑制的化学特征。基于这些化学特征，获得了具有代表性特征的药效团，用于筛选 BindingDB 数据库。通过 Lipinski 过滤器筛选与药效团模型匹配的化合物。通过瑞士 ADME 网络服务器和 ProTox-II-化学毒性预测网络服务器预测通过 Lipinski 过滤器的化合物的 ADMET 特性。选择的化合物进行分子对接研究。
在 PLIF 研究中，表明十五个抑制剂与 PDEδ 的 prenyl-结合口袋中的残基 Met20、Trp32、Ile53、Cys56、Lys57、Arg61、Gln78、Val80、Glu88、Ile109、Ala11、Met117、Met118、Ile129、Thr131 和 Tyr149 形成相互作用。基于这些化学特征，具有代表性特征的药效团由三个键受体、两个疏水性元素和一个氢键供体组成。当药效团模型用于虚拟筛选 Binding DB 数据库时，选择了 2532 种化合物。然后，通过 Lipinski 规则过滤器筛选 2532 种化合物。在 2532 种化合物中，有两种化合物符合 Lipinski 规则。随后，对两种化合物的 ADMET 特性和药物特性进行了比较。最后，选择化合物 2 进行分子对接分析，并作为潜在的 PDEδ 抑制剂。
通过结构基于药效团生成、药效团基于虚拟筛选和分子对接的组合发现的命中化合物与 PDEδ 疏水口袋中的关键氨基酸相互作用，导致发现一种新型支架作为 PDEδ 的潜在抑制剂。
