一个综合的环境毒性风险评估框架,结合深度学习和分子模拟:皮雷和乳腺癌的案例研究
Jinghui Sung1,2, Zikang Jiang1,2, Wen-Pei Sung3
1Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China.
Biochemistry and biophysics reports
|August 18, 2025
概括
天然杀虫剂甲I和II可能会通过结合参与瘤生长的关键蛋白增加乳腺癌风险. 这项研究开发了一个计算框架,以追踪这些分子相互作用的临床结果,帮助毒理政策.
科学领域:
- 计算毒理学计算毒理学
- 分子建模分子建模
- 癌症研究 癌症研究
背景情况:
- 天然杀虫剂如甲I和II被广泛使用,但它们的潜在健康风险,特别是乳腺癌,需要进行系统的调查.
- 了解将环境暴露与癌症发展联系在一起的分子机制对于公共卫生和政策制定至关重要.
研究的目的:
- 开发和验证一个整合性的计算毒理学框架,以评估甲素I和II与乳腺癌风险之间的关联.
- 建立一个可追溯的风险推断链,从分子相互作用到临床结果.
主要方法:
- 集成深度学习来预测药物向相互作用 (DeepPurpose).
- 分子对接和分子动力学 (MD) 模拟的应用.
- 使用蛋白质与蛋白质相互作用 (PPI) 网络建模和跨度风险指标建模.
主要成果:
- 甲I显示了与乳腺癌相关的蛋白质RPS6KB1,TNKS2和MAOB (ΔG高达-27.37 kcal/mol) 的高亲和度结合.
- 确定了致癌途径 (PI3K/AKT,Wnt/β-catenin) 的潜在调节和代谢重编程.
- 与ER+乳腺癌相关的RPS6KB1,与侵袭性TNBC相关的TNKS2以及MAOB在毒理学建模中显示出高度复杂的稳定性.
结论:
- 开发的多尺度框架提供了从化合物结构到临床风险的机械可解释的联系.
- 该方法支持环境暴露监测,并为天然农药的毒理政策提供信息.
- 该开源工具链可用于评估其他天然化合物和环境污染物.
关键词:
对药物目标相互作用的深度学习环境化学风险评估环境化学风险评估综合计算毒理学 综合计算毒理学机械学因果推理推理.分子对接和分子动力学多omics数据集成多omics数据集成多尺度分子模拟多尺度分子模拟更多相关视频
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