从核受体到GPCR:一种深度转移学习方法,用于增强环境雌激素识别
Tingji Yao1,2, Jiaqi Luo1, Xiaoxiao Han1
1State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
Environmental science & technology
|April 30, 2025
概括
一个新的深度转移学习模型,GPNET,准确地预测了与G蛋白结合雌激素受体 (GPER) 结合的环境雌激素. 这种方法克服了有限的GPER配体数据带来的挑战,有助于发现新的内分泌干扰化学相互作用.
科学领域:
- 内分泌学 在内分泌学.
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 环境雌激素 (EEs) 是内分泌干扰化学物质 (EDC),与雌激素受体 (ERs) 和G蛋白结合雌激素受体 (GPER) 相互作用.
- 虽然GPER调解了快速的非基因组效应,但有限的配体数据阻碍了预测查模型的开发.
- 了解EEs与GPER的相互作用对于评估其健康风险至关重要.
研究的目的:
- 开发一种新的深度转移学习模型 (GPNET) 用于预测GPER结合联体.
- 为了利用GPER和ER之间的功能相似性,克服GPER连接体预测的数据稀缺性.
- 为GPER联体结合提供机理性见解,包括双A (BPA) 替代品.
主要方法:
- 使用3D分子表面静电电位点云 (SepPC) 作为输入,构建了一个深度转移学习模型 (GPNET).
- 利用ER联体的知识来预训练模型,然后用有限的GPER联体数据进行微调.
- 对GPNET与非传输模型的性能进行了评估,在验证和测试集上获得了高AUC_ROC值.
主要成果:
- 在一个小的数据集上,GPNET的表现明显超过了"从头开始"模型.
- 在验证组中达到0.898的AUC_ROC,在测试组中达到0.863,显示出高预测准确度.
- 视觉化显示了关键的结合特征,并为与GPER相互作用的两个BPA替代品提供了机制解释.
结论:
- 深度转移学习对于预测GPER联体是有效的,特别是在有限的数据.
- GPNET提供了一个强大的工具,用于选潜在的GPER结合环境雌激素.
- 这项研究提高了对EDC分子机制的理解,并有助于开发更安全的替代品.
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