Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany.
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Egyptian Russian University, Cairo 11829, Egypt.
Molecules. 2022 Apr 14;27(8):2526. doi: 10.3390/molecules27082526.
Class I histone deacetylases, HDAC1, HDAC2, and HDAC3, represent potential targets for cancer treatment. However, the development of isoform-selective drugs for these enzymes remains challenging due to their high sequence and structural similarity. In the current study, we applied a computational approach to predict the selectivity profile of developed inhibitors. Molecular docking followed by MD simulation and calculation of binding free energy was performed for a dataset of 2-aminobenzamides comprising 30 previously developed inhibitors. For each HDAC isoform, a significant correlation was found between the binding free energy values and in vitro inhibitory activities. The predictive accuracy and reliability of the best preforming models were assessed on an external test set of newly designed and synthesized inhibitors. The developed binding free-energy models are cost-effective methods and help to reduce the time required to prioritize compounds for further studies.
I 类组蛋白去乙酰化酶(HDAC1、HDAC2 和 HDAC3)是癌症治疗的潜在靶点。然而,由于这些酶具有高度的序列和结构相似性,开发对其具有选择性的药物仍然具有挑战性。在本研究中,我们应用计算方法来预测已开发抑制剂的选择性特征。我们对包含 30 种先前开发的抑制剂的 2-氨基苯甲酰胺数据集进行了分子对接,随后进行了 MD 模拟和结合自由能计算。对于每种 HDAC 同工酶,均发现结合自由能值与体外抑制活性之间存在显著相关性。我们还在一组新设计和合成的抑制剂的外部测试集中评估了最佳表现模型的预测准确性和可靠性。开发的结合自由能模型是一种具有成本效益的方法,有助于减少优先考虑进一步研究的化合物所需的时间。