Bian Wengong, Guo Yaxin
Department of Anesthesiology, Shandong Provincial Third Hospital, Jinan, Shandong, China.
Front Pharmacol. 2025 Jun 27;16:1590711. doi: 10.3389/fphar.2025.1590711. eCollection 2025.
Defective in cullin neddylation 1 (DCN1) plays a pivotal role in anticardiac fibrosis by interacting with UBC12 and catalyzing cullin neddylation, which activates cullin-RING E3 ligases (CRLs). As a key modulator of anticardiac fibrosis, DCN1 has emerged as an attractive target for therapeutic intervention. The aim of this study is to design and evaluate novel DCN1 inhibitors using a combination of three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations.
A dataset of 47 derivatives was employed to construct Comparative Molecular Field Analysis (COMSIA) model, incorporating steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor fields to accurately predict compound activity. In silico molecular docking studies, selected compounds were docked with the target protein to evaluate their binding affinity. Additionally, molecular dynamics simulations were performed to assess the stability of the compounds, followed by energy decomposition analysis was used to identify key residues contributing to binding.
The comparative molecular similarity index analysis (COMSIA) model achieved a cross-validated q of 0.553, a non-cross-validated r of 0.959, and an value of 0.766, demonstrating good accuracy and stability in predicting the activity of the compounds. The top compound exhibited a predicted pIC50 of 9.674 and showed strong binding affinity in molecular docking. Molecular dynamics simulations confirmed the stability of the compound at the binding site, while energy decomposition analysis identified key residues essential for binding interaction.
This study successfully designed and evaluated novel DCN1 inhibitors using an integrated approach that combines 3D-QSAR modeling, molecular docking, and molecular dynamics simulations. The findings provide an effective computational platform for the design of DCN1 inhibitors and lay a solid foundation for the development of drugs targeting anticardiac fibrosis.
泛素结合酶12（UBC12）相互作用并催化cullin类泛素化修饰，从而激活cullin-RING E3连接酶（CRLs），在抗心肌纤维化过程中发挥关键作用。作为抗心肌纤维化的关键调节因子，DCN1已成为一个有吸引力的治疗干预靶点。本研究旨在结合三维定量构效关系（3D-QSAR）建模、分子对接和分子动力学模拟来设计和评估新型DCN1抑制剂。
采用包含47种衍生物的数据集构建比较分子场分析（COMSIA）模型，纳入空间、静电、疏水、氢键供体和受体场以准确预测化合物活性。在计算机模拟分子对接研究中，将选定化合物与靶蛋白对接以评估其结合亲和力。此外，进行分子动力学模拟以评估化合物的稳定性，随后通过能量分解分析来确定对结合有贡献的关键残基。
比较分子相似性指数分析（COMSIA）模型的交叉验证q值为0.553，非交叉验证r值为0.959，值为0.766，表明在预测化合物活性方面具有良好的准确性和稳定性。顶级化合物的预测pIC50为9.674，在分子对接中显示出很强的结合亲和力。分子动力学模拟证实了该化合物在结合位点的稳定性，而能量分解分析确定了结合相互作用所必需的关键残基。
本研究成功地使用结合3D-QSAR建模、分子对接和分子动力学模拟的综合方法设计和评估了新型DCN1抑制剂。这些发现为DCN1抑制剂的设计提供了一个有效的计算平台，并为开发抗心肌纤维化的药物奠定了坚实基础。
