整合蛋白质语言和几何深度学习模型,以提高疫苗抗原预测能力
Xiaodong Zai1, Yunxiang Zhao1, Xiaolin Wang1
1Laboratory of Advanced Biotechnology, Beijing Institute of Biotechnology, Beijing, China.
Nature communications
|December 21, 2025
概括
我们开发了PLGDL,一种新的AI框架,使用蛋白质语言和几何深度学习来预测保护性疫苗抗原. 该工具通过识别关键病原体蛋白质来加速疫苗开发,包括新的Mpox抗原G10R.
科学领域:
- 免疫学和疫苗学 免疫学和疫苗学
- 计算生物学和生物信息学
- 传染病研究 传染病研究
背景情况:
- 疫苗开发依赖于从病原体中识别有效的向抗原.
- 手动抗原选择耗时且容易产生偏见.
- 人工智能为简化抗原发现提供了一种有前途的方法.
研究的目的:
- 开发和验证一个人工智能框架,PLGDL,用于预测保护性疫苗抗原.
- 整合蛋白质序列和结构特征,以提高抗原预测的准确性.
- 证明该框架在识别各种病原体的抗原方面的实用性,包括Mpox.
主要方法:
- 开发了PLGDL框架,结合了蛋白质语言模型和几何深度学习.
- 利用了原始氨基酸序列和抗原的3D结构数据.
- 在各种构建和公共数据集上验证了模型,包括病毒,细菌和真核病原体.
主要成果:
- 在各种病原体类型中,PLGDL框架表现出强大的性能.
- 该模型成功识别了已知的保护性抗原.
- 在正在爆发的疫情期间,对Mpox病毒发现了一种新的保护性抗原G10R.
结论:
- PLGDL提供了一种高性能,人工智能驱动的工具,用于快速查保护性疫苗抗原.
- 蛋白质语言和几何深度学习的协同使用减少了偏差并增强了预测.
- 这种方法为加速针对新兴传染病的疫苗开发提供了显著的进步.
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