Hayama Teppei, Sugawara Rin, Kamata Ryo, Sekijima Masakazu, Takeda Kazuki
Laboratory of Toxicology, School of Veterinary Medicine, Kitasato University.
Department of Computer Science, Institute of Science Tokyo.
J Toxicol Sci. 2025;50(7):309-324. doi: 10.2131/jts.50.309.
Identifying the molecular targets of toxic compounds remains a major challenge in toxicology, particularly when adverse effects occur in off-target organs and the mechanism of action is unknown. To address this issue, a comprehensive computational pipeline was developed to perform high-throughput molecular docking across the entire AlphaFold2-predicted structural proteome of representative organisms such as human and mouse, followed by enrichment analysis to estimate biological processes potentially affected by ligand binding. The pipeline was first evaluated using six known drug-target pairs. In several cases, the known targets were ranked between the top 2 and 250 proteins (top 0.009-1.15%) among more than 21,000 proteins, and displayed docking poses consistent with experimentally observed binding conformations. However, performance was limited for certain targets, such as carbonic anhydrase II with acetazolamide, where the binding pocket was broad, leading to inaccurate docking results. The pipeline was subsequently applied to puberulic acid, a compound suspected of causing severe nephrotoxicity. Screening identified sodium/myo-inositol cotransporter 2 (SLC5A11) as a high-affinity target in both human and mouse, suggesting a mechanism involving disruption of renal osmoregulation. Although docking scores represent only theoretical binding estimates and do not directly imply physiological effects, their distribution was independent of protein length and AlphaFold2 confidence scores (pLDDT), supporting the methodological robustness. This in silico framework enables hypothesis-driven identification of potential target proteins for toxicants or therapeutics and offers a useful tool for predictive toxicology, particularly when experimental data are limited. The pipeline is available at: https://github.com/toxtoxcat/reAlldock.
确定有毒化合物的分子靶点仍然是毒理学中的一项重大挑战,尤其是当不良反应发生在非靶器官且作用机制不明时。为解决这一问题,我们开发了一个综合计算流程,以对人类和小鼠等代表性生物体的整个AlphaFold2预测结构蛋白质组进行高通量分子对接,随后进行富集分析,以估计可能受配体结合影响的生物学过程。该流程首先使用六对已知的药物-靶点对进行评估。在几种情况下,已知靶点在超过21,000种蛋白质中排名在前2至250种蛋白质之间(前0.009 - 1.15%),并且显示出与实验观察到的结合构象一致的对接姿势。然而,对于某些靶点,如乙酰唑胺与碳酸酐酶II的情况,其结合口袋较宽,导致对接结果不准确,性能受到限制。该流程随后应用于疑似导致严重肾毒性的化合物puberulic acid。筛选确定钠/肌醇共转运蛋白2(SLC5A11)是人类和小鼠中的高亲和力靶点,提示了一种涉及破坏肾脏渗透调节的机制。尽管对接分数仅代表理论结合估计,并不直接意味着生理效应,但其分布与蛋白质长度和AlphaFold2置信度分数(pLDDT)无关,支持了该方法的稳健性。这种计算机模拟框架能够以假设驱动的方式识别有毒物质或治疗药物的潜在靶点蛋白,并为预测毒理学提供了一个有用的工具,特别是在实验数据有限的情况下。该流程可在以下网址获取:https://github.com/toxtoxcat/reAlldock。