Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Cells. 2024 Apr 30;13(9):771. doi: 10.3390/cells13090771.
Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3-.
肿瘤坏死因子-α诱导蛋白 8 样 3（TNFAIP8L3 或 TIPE3）作为脂质第二信使的转移蛋白发挥作用。TIPE3 在几种人类癌症中高度上调，并已被证实可显著促进肿瘤细胞增殖、迁移和侵袭，并抑制癌细胞凋亡。因此，抑制 TIPE3 的功能有望成为对抗癌症的有效策略。人工智能（AI）驱动的药物开发的进步最近激发了抗癌药物开发的研究。在这项工作中，我们将 DFCNN、Autodock Vina 对接、DeepBindBC、MD 和元动力学结合起来，从 ZINC 化合物数据集中高效识别 TIPE3 的抑制剂。选择了六个有潜力的候选者进行进一步的实验研究，以验证它们的抗肿瘤活性。在这些候选者中，三种小分子化合物（K784-8160、E745-0011 和 7238-1516）在体外表现出显著的抗肿瘤活性，导致肿瘤细胞活力、增殖和迁移减少，凋亡肿瘤细胞死亡增加。值得注意的是，E745-0011 和 7238-1516 对高表达 TIPE3 的肿瘤细胞表现出选择性细胞毒性，而对正常人类细胞或低表达 TIPE3 的肿瘤细胞几乎没有影响或没有影响。分子对接分析进一步支持了它们与 TIPE3 的相互作用，强调了疏水性相互作用及其共享的相互作用残基，并为设计更有效的抑制剂提供了见解。总之，这项工作证明了将深度学习和 MD 模拟纳入虚拟药物筛选的可行性，并提供了具有针对 TIPE3 开发抗癌药物的显著潜力的抑制剂。
