Computational investigation of adenosine 5′- (α, β-methylene)- diphosphate (AMPCP) derivatives as ecto-5′-nucleotidase (CD73) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations
Computational investigation of adenosine 5′- (α, β-methylene)- diphosphate (AMPCP) derivatives as ecto-5′-nucleotidase (CD73) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations
复制标题
使用 3D-QSAR、分子对接和分子动力学模拟对腺苷 5α-（α，β-亚甲基）-二磷酸 (AMPCP) 衍生物作为 ecto-5α-核苷酸酶 (CD73) 抑制剂进行计算研究
DOI：
10.1007/s11224-021-01863-2
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发表时间：
2022
影响因子：
1.7
通讯作者：
Li Ning
中科院分区：
文献类型：
--
作者：
Wen Jiatong;Zhang Heng;Meng Churen;Zhou Di;Chen Gang;Wang Jian;Liu Yang;Yuan Lei;Li Ning
CD73作为一种通过糖基磷脂酰肌醇（GPI）锚定在细胞膜外侧的表面酶，可以将肿瘤细胞微环境中的AMP转化为腺苷，促进肿瘤细胞的生长。它在许多不同类型的人类肿瘤中过度表达，例如胃癌、胰腺癌、肝癌和其他肿瘤细胞。因此，CD73的靶向抑制剂被认为是潜在的肿瘤治疗方法。由于核苷CD73抑制剂的生物利用度较低，因此有必要开发新的抑制剂。本研究通过分子对接、三维定量构效关系（3D-QSAR）和分子动力学（MD）模拟，对一系列CD73抑制剂进行了计算和研究，揭示了它们的构效关系。通过分子对接研究，探索抑制剂与蛋白质之间可能的相互作用模式。随后，通过比较分子场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）建立了3D-QSAR模型。对于最佳 CoMFA 模型，Q2 和 R2 值分别为 0.708 和 0.983，而对于最佳 CoMSIA 模型，Q2 和 R2 值分别为 0.809 和 0.992。根据等值线图，我们设计了十种新的CD73抑制剂，并通过模型预测了它们的活性，它们都优于数据集中的分子。此外，为了选择潜在的候选药物，对模板分子和设计的化合物进行了 ADMET 预测。此外，通过分子动力学模拟评估了两种抑制剂与CD73形成的复合物的稳定性，结果与分子对接和3D-QSAR研究结果一致。最后通过比表面积法（MM-GBSA）计算结合自由能，结果与分子与CD73蛋白结合过程中范德华和库仑贡献最大的活性一致。总之，我们的研究为CD73抑制剂的进一步开发提供了有价值的信息。
CD73, as a surface enzyme anchored on the outside of the cell membrane via glycosylphosphatidylinositol (GPI), can convert the AMP in the tumor cell microenvironment into adenosine to promote the growth of tumor cells. It has been overexpressed in many different types of human tumors, such as gastric cancer, pancreatic cancer, liver cancer, and other tumor cells. Therefore, targeted inhibitors of CD73 are considered potential tumor treatment methods. Due to the low bioavailability of nucleoside CD73 inhibitors, it is necessary to develop new inhibitors. In this study, through molecular docking, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular dynamics (MD) simulations, a series of CD73 inhibitors were calculated and studied to reveal their structure–activity relationships. Through molecular docking studies, the possible mode of interaction between inhibitors and protein is explored. Subsequently, a 3D-QSAR model was established by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the best CoMFA model, theQ2andR2values are 0.708 and 0.983, respectively, while for the best CoMSIA model, theQ2andR2values are 0.809 and 0.992, respectively. Based on the contour maps, we designed ten new CD73 inhibitors and predicted their activity by the model, all of them are better than molecules in the dataset. In addition, in order to select potential drug candidates, ADMET prediction was performed on template molecules and designed compounds. Moreover, the stability of the complex formed by the two inhibitors and CD73 was evaluated by molecular dynamics simulation, and the results are consistent with the results of molecular docking and 3D-QSAR research. Finally, the binding free energy was calculated by the surface area method (MM-GBSA), and the results are consistent with the activities that van der Waals and Coulomb contribute the most during the binding process of the molecule to the CD73 protein. In conclusion, our research provides valuable information for the further development of CD73 inhibitors.