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通过基于结构的多步药物设计鉴定潜在的甲基转移酶NSD2酶抑制剂。

Identification of potential methyltransferase NSD2 enzymatic inhibitors through a multi-step structure-based drug design.

作者信息

Shen Yunpeng, Zhang Yingying, Wu Tongyi, Zhang Lixue, Belviso Benny Danilo

机构信息

Department of Biotechnology, School of Biological Engineering, Henan University of Technology, Henan Province, Zhengzhou, 450001, People's Republic of China.

Department of Biotechnology, School of International Education, Henan University of Technology, Henan Province, Zhengzhou, 450001, People's Republic of China.

出版信息

Mol Divers. 2024 Dec 7. doi: 10.1007/s11030-024-11072-8.

Abstract

Reversing aberrant protein methylation levels is widely recognized as a key focus in cancer therapy. As an essential lysine methylation regulator, NSD2 (Nuclear receptor-binding SET Domain 2, also known as WHSC1/MMSET) regulates chromatin structural sparsity and DNA repair processes. Abnormal enhancement of NSD2 methylation activity (caused by NSD2 overexpression and point mutations) has been closely related to the initiation and development of various cancers and diseases. However, the lack of selective inhibitors hinders further therapeutic intervention and limits the exploration of its biological mechanism. Therefore, this study developed an integrated approach that includes binding feature pharmacophore modeling, gradient database screening of 120 million compounds, flexible docking, and molecular dynamic simulation. This approach was used to identify hit compounds targeting the substrate/coenzyme binding site of NSD2. Subsequently, 20 lead compounds were retrieved by using molecular docking analysis and ADMET prediction. Finally, MD simulations were performed to validate the binding stability of selected drug candidates. The findings indicated that these newly obtained compounds might be potent NSD2 inhibitors. We hope the integrated virtual screening approach will provide a valuable idea for discovering novel H3K36 methyltransferase inhibitors.

摘要

逆转异常的蛋白质甲基化水平已被广泛认为是癌症治疗的关键重点。作为一种重要的赖氨酸甲基化调节剂,核受体结合SET结构域2(NSD2,也称为WHSC1/MMSET)调节染色质结构稀疏性和DNA修复过程。NSD2甲基化活性的异常增强(由NSD2过表达和点突变引起)与各种癌症和疾病的发生发展密切相关。然而,缺乏选择性抑制剂阻碍了进一步的治疗干预,并限制了对其生物学机制的探索。因此,本研究开发了一种综合方法,包括结合特征药效团建模、对1.2亿种化合物进行梯度数据库筛选、柔性对接和分子动力学模拟。该方法用于识别靶向NSD2底物/辅酶结合位点的命中化合物。随后,通过分子对接分析和ADMET预测检索到20种先导化合物。最后,进行分子动力学模拟以验证所选候选药物的结合稳定性。研究结果表明,这些新获得的化合物可能是有效的NSD2抑制剂。我们希望这种综合虚拟筛选方法将为发现新型H3K36甲基转移酶抑制剂提供有价值的思路。

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