植物病原体效应生物在勃发展的AI时代
Darcy Adam Bain Jones1, Sylvain Raffaele1
1Laboratoire des Interactions Plantes-Microbes-Environnement (LIPME), INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, France; email: darcy.jones@inrae.fr, sylvain.raffaele@inrae.fr.
Annual review of phytopathology
|June 20, 2025
概括
人工智能 (AI) 有助于识别和理解植物病原体效应蛋白. 人工智能工具加速了对效应器功能的研究和演变的研究,即使没有明确的序列相似性.
科学领域:
- 植物病理学 植物病理学
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 植物病原体分泌效应蛋白来操纵宿主在感染期间的反应.
- 由于它们的多样性,快速进化和宿主特异性相互作用,鉴定这些效应物的特征是困难的.
研究的目的:
- 审查人工智能 (AI) 在效应生物学的应用.
- 突出AI在识别,功能性表征和理解效应蛋白的演变方面的潜力.
主要方法:
- 关于人工智能在蛋白质生物学方面的最新进展的综述.
- 专注于人工智能应用程序,如细胞下本地化预测,蛋白质结构建模 (例如,AlphaFold) 和蛋白质语言模型.
主要成果:
- 人工智能提供了新的方法来克服对效应蛋白特征的挑战.
- 人工智能工具可以预测效应器的本地化,结构和功能,补充实验方法.
结论:
- 人工智能显著加速了对效应蛋白功能和进化史的研究.
- 人工智能对于研究效应因子是有价值的,即使序列相似性或已知的域不存在.
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