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PAIRNet:通过位置感知RNA相互作用建模预测PIWI裂变特异性

Lin Zeng¹, Zhenzhen Li², Enzhi Shen²

¹Center for Cognitive Machines and Computational Health (CMaCH), School of Computer Science, Shanghai Jiao Tong University, Shanghai, China.

PLoS computational biology

|February 19, 2026

概括

深度学习框架PAIRNet通过建模导向-目标相互作用,准确地预测PIWI介导的RNA裂变率. 这种计算工具增强了对piRNA沉默的理解,并加速了基因组防御研究.

科学领域:

分子生物学分子生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

通过piRNA引导的RNA分裂,PIWI蛋白对基因组完整性至关重要.
Cleave-N'-Seq (CNS-seq) 映射了PIWI的目标,但具有劳动密集型的工作流.
在PIWI准中对序列决定因素的系统探索是有限的.

研究的目的:

开发PAIRNet,这是一个深度学习框架,用于预测PIWI介导的RNA分裂率.
为了建模导向-目标相互作用,考虑几何和序列.
加速对RNA导向基因组防御的机制研究.

主要方法:

开发了PAIRNet,这是一个集生化学见解和计算方法的深度学习框架.
编码的配对状态,不匹配,插入,删除和位置嵌入.
采用混合CNN-变压器架构来优先考虑双重动态.
集成的可解释性模块 (销售权图,反事实分析).

主要成果:

在四个PIWI指导数据集中,PAIRNet准确地预测PIWI介导的RNA分裂率.
与现有方法相比,在PCC方面取得了显著的改进 (34.7%用于MILI,14.6%用于MIWI).
总结了关键的生物学原理,包括对催化残留的严格互补性和3'不匹配容忍度.

结论:

PAIRNet将生物化学精度与PIWI目标分析的计算可扩展性相结合.
建立了设计高特异性piRNA沉默工具的路线图.
加快对RNA导向基因组防御的机制研究.

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