Zhang Jungan, Ren Yixin, Teng Yun, Wu Han, Xue Jingsu, Chen Lulu, Song Xiaoyue, Li Yan, Zhou Ying, Pang Zongran, Wang Hao
School of Pharmacy, Minzu University of China, Beijing, China.
Institute of National Security, Minzu University of China, Beijing, China.
Front Chem. 2025 Jan 20;13:1548812. doi: 10.3389/fchem.2025.1548812. eCollection 2025.
Protein arginine methyltransferases (PRMTs) play crucial roles in gene regulation, signal transduction, mRNA splicing, DNA repair, cell differentiation, and embryonic development. Due to its significant impact, PRMTs is a target for the prevention and treatment of various diseases. Among the PRMT family, PRMT1 is the most abundant and ubiquitously expressed in the human body. Although extensive research has been conducted on PRMT1, the reported inhibitors have not successfully passed clinical trials. In this study, deep learning was employed to analyze the characteristics of existing PRMTs inhibitors and to construct a classification model for PRMT1 inhibitors. Through a classification model and molecular docking, a series of potential PRMT1 inhibitors were identified. The representative compound (compound 156) demonstrates stable binding to the PRMT1 protein by molecular hybridization, molecular dynamics simulations, and binding free energy analyses. The study discovered novel scaffolds for potential PRMT1 inhibitors.
蛋白质精氨酸甲基转移酶（PRMTs）在基因调控、信号转导、mRNA剪接、DNA修复、细胞分化和胚胎发育中发挥着关键作用。由于其具有重大影响，PRMTs成为预防和治疗各种疾病的靶点。在PRMT家族中，PRMT1在人体中表达最为丰富且分布广泛。尽管对PRMT1已进行了广泛研究，但报道的抑制剂尚未成功通过临床试验。在本研究中，采用深度学习来分析现有PRMTs抑制剂的特征，并构建PRMT1抑制剂的分类模型。通过分类模型和分子对接，鉴定出一系列潜在的PRMT1抑制剂。代表性化合物（化合物156）通过分子杂交、分子动力学模拟和结合自由能分析，显示出与PRMT1蛋白的稳定结合。该研究发现了潜在PRMT1抑制剂的新型骨架。
