蛋白质与蛋白质相互作用的快速蛋白质广泛发现与ppIRIS
Luiz Felipe Piochi1, Di Tang2, Johan Malmström2,3
1Université de Lorraine, CNRS, Inria, LORIA, Nancy, France.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 12, 2026
概括
我们开发了ppIRIS,这是一个深度学习工具,可以从序列中预测蛋白质与蛋白质相互作用 (PPI). 它可以快速,准确地对细菌和跨物种的PPI进行全蛋白体查.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对细胞功能和宿主-病原体相互作用至关重要.
- 绘制完整的交互原子是具有挑战性的,因为实验和计算方法的局限性.
- 现有的计算工具往往缺乏广泛的适用性,或者在计算上昂贵.
研究的目的:
- 引入ppIRIS,这是一个新的深度学习框架,用于从蛋白质序列直接预测PPI.
- 为了使PPI的有效和准确的蛋白质组范围的查.
- 促进在细菌和跨物种中发现新型PPI.
主要方法:
- ppIRIS集成了使用轻量级深度学习架构 (代罗网络) 的进化和结构嵌入.
- 该框架可以从氨基酸序列直接预测PPI.
- 该模型使用细菌数据集进行训练,并根据多种基准进行评估.
主要成果:
- ppIRIS在多种PPI预测基准中实现了最先进的准确性.
- 该工具在几分钟内实现了全蛋白质组选,识别了与毒性相关的A组链球菌 (GAS) 中的功能集群.
- 跨物种预测发现了显著的GAS-人体血相互作用,丰富了与免疫相关的途径,并实验验证了新的预测.
结论:
- ppIRIS是一个强大而有效的工具,用于系统地发现细菌和跨物种的PPI.
- 该框架通过提供可扩展和可泛化的计算方法来推进互动组映射.
- 已识别的PPI提供了细菌毒性和宿主-病原体动态的见解.
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