一种基于变压器的深度学习方法来预测空气有机污染物-人类蛋白质相互作用
Yan Zhu1,2,3, Shihao Wang4, Yong Han4
1Hong Kong Jockey Club STEM Laboratory of Genomics and AI in Healthcare, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Environmental science & technology
|December 11, 2025
概括
一个新的深度学习模型,tipFormer,准确地预测了污染物-蛋白质相互作用,进步了我们对空气污染的理解.
科学领域:
- 环境健康 环境健康
- 计算生物学 计算生物学
- 毒理学 毒理学 毒理学
背景情况:
- 空气污染是全球主要的健康问题.
- 了解污染物和蛋白质之间的分子相互作用对于评估毒性至关重要.
- 目前的方法很难识别这些相互作用,特别是对于新污染物.
研究的目的:
- 开发一种新的深度学习模型,用于预测空气中的有机污染物与蛋白质相互作用.
- 加强对空气污染毒性的机制性理解和风险评估.
主要方法:
- 开发了tipFormer,这是一个使用双重预训练语言模型和交叉注意力的深度学习模型.
- 编码蛋白质和有机污染物以捕捉相互作用模式.
- 在人类支气管上皮细胞中使用全基因组转录组分析验证的预测.
主要成果:
- tipFormer在一个测试套件上实现了最先进的性能,AUC为0.9787.
- 预测的污染物目标与实验性响应基因显著一致.
- 证明了对空气污染影响的生物相关性和机械洞察力.
结论:
- tipFormer提供了一种强大的计算方法来预测污染物-蛋白质相互作用.
- 这项研究为空气污染毒性的分子基础提供了更深入的机械洞察力.
- 这项工作将计算预测与实验验证联系起来,以改善风险评估.
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