Jia Xiaowei, Liu Meng, Tang Yushi, Meng Jingyan, Fang Ruolin, Wang Xiting, Li Cheng
School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Sijiqing Hospital, Beijing, China.
Sci Rep. 2025 Mar 27;15(1):10540. doi: 10.1038/s41598-025-95530-9.
The role of LOXL2 in cancer has been widely demonstrated, but current therapies targeting LOXL2 are not yet fully developed. We believe that selective nature-derived inhibition of LOXL2 may provide a better therapeutic approach for the treatment of cancer. Therefore, we adopted a comprehensive approach combining deep learning and traditional computer-aided drug design methods to screen LOXL2 selective inhibitors. Bioactivity and affinity of the potential LOXL2 inhibitors were determined by molecular docking and virtual screening. At the same time, we experimentally tested the effect of potential LOXL2 inhibitors on cancer cells. Validation showed that it could inhibit proliferation and migration, promote apoptosis of CT26 cells, and reduce the expression level of LOXL2 protein. As a result, we identified a potent LOXL2 inhibitor: the natural product Forsythoside A, and demonstrated that Forsythoside A has an inhibitory effect on tumors.
LOXL2在癌症中的作用已得到广泛证实，但目前针对LOXL2的疗法尚未完全开发出来。我们认为，对LOXL2进行选择性的天然来源抑制可能为癌症治疗提供一种更好的治疗方法。因此，我们采用了深度学习与传统计算机辅助药物设计方法相结合的综合方法来筛选LOXL2选择性抑制剂。通过分子对接和虚拟筛选确定潜在LOXL2抑制剂的生物活性和亲和力。同时，我们通过实验测试了潜在LOXL2抑制剂对癌细胞的作用。验证表明，它可以抑制CT26细胞的增殖和迁移，促进其凋亡，并降低LOXL2蛋白的表达水平。结果，我们鉴定出一种有效的LOXL2抑制剂：天然产物连翘酯苷A，并证明连翘酯苷A对肿瘤具有抑制作用。
