PPEPFinder:一个深度学习框架,集成序列嵌入和结构图表表示,用于预测真菌和虫效应蛋白
Mengdi Yuan1, Shaoke Zhang1, Jiajun Li1
1State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
The Plant journal : for cell and molecular biology
|November 24, 2025
概括
准确识别植物病原体效应蛋白对于抗病能力至关重要. 一个新的深度学习框架,PPEPFinder,利用序列和结构数据进行高级预测,改进生物信息学方法.
科学领域:
- 植物病理学 植物病理学
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 植物病原体分泌效应蛋白来殖民宿主并抑制免疫力,导致疾病.
- 实验性识别效应蛋白是劳动密集型和昂贵的.
- 深度学习和蛋白质语言模型提供了新的生物信息学解决方案.
研究的目的:
- 开发一个先进的深度学习框架,PPEPFinder,用于预测真菌和菌体中的效应蛋白.
- 与现有方法相比,提高效应蛋白识别的准确性和效率.
主要方法:
- 开发了PPEPFinder,这是一个使用序列和结构信息的集成深度学习框架.
- 采用基于序列的变压器模型与进化规模建模 (ESM) 嵌入.
- 使用了两个基于结构的图形注意力网络模型,并嵌入了ESM或SaProt.
- 综合预测使用后勤回归模型进行最终得分.
主要成果:
- 在预测效应蛋白质方面,PPEPFinder表现出卓越的性能.
- 该框架有效地利用了序列和结构信息.
- 性能优于现有的最先进的效应器预测工具.
结论:
- PPEPFinder提供了一种强大而准确的生物信息学工具,用于预测效应蛋白.
- 该框架增强了对植物病原体机制的理解.
- 促进新型疾病耐药性策略的开发.
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