Source data for ablation study.

PIWI proteins maintain genome integrity by piRNA-guided cleavage of complementary RNA targets. While Cleave-N’-Seq (CNS-seq) has advanced our understanding of PIWI targeting logic through quantitative mapping of cleavage rates and pairing rules, its labor-intensive workflows hinder systematic exploration of sequence determinants. Here, we present PAIRNet, a deep learning framework that predicts PIWI-mediated RNA cleavage rates by explicitly modeling guide-target interactions. Recognizing that interaction geometry, not just sequence, dictates cleavage efficiency, PAIRNet integrates biochemical insights with computational innovation: it encodes pairing states, mismatch types, insertions, and deletions alongside learnable positional embeddings to quantify spatial dependencies; employs a hybrid CNN-Transformer architecture prioritizing duplex dynamics over static sequence features to resolve both local catalytic motifs (e.g., contiguous base-pairing at g10–g11) and distal structural perturbations; and incorporates interpretability modules (saliency maps, counterfactual analysis) to link interaction patterns to biochemical insights and uncover position-specific cleavage rules. Validated across four PIWI-guide datasets, PAIRNet consistently ranks among the top two performers in all experimental conditions, achieving the most pronounced relative improvements in PCC, 34.7% for MILI and 14.6% for MIWI, over second-ranking methods. Critically, PAIRNet recapitulates key biological principles—stringent complementarity at catalytic residues (g10–g11) and tolerance for 3’ mismatches—aligning with structural studies of PIWI dynamics. By bridging biochemical precision with computational scalability, PAIRNet establishes a roadmap for designing high-specificity piRNA silencing tools while accelerating mechanistic studies of RNA-guided genome defense.PIWI蛋白（PIWI）通过piRNA（Piwi-interacting RNA）引导的互补RNA靶标切割来维持基因组完整性。尽管Cleave-N’-Seq（CNS-seq）通过对切割速率与配对规则的定量图谱绘制，推动了我们对PIWI靶向逻辑的认知，但其劳动密集型的实验流程阻碍了对序列决定因素的系统性探索。在此，我们提出PAIRNet——一种通过显式建模向导-靶标相互作用来预测PIWI介导的RNA切割速率的深度学习框架。研究认识到，决定切割效率的不仅是序列，还有相互作用的几何特征；PAIRNet将生化认知与计算创新相结合：它对配对状态、错配类型、插入与缺失，结合可学习位置嵌入（positional embeddings）进行编码，以量化空间依赖性；采用混合卷积神经网络（Convolutional Neural Network, CNN）-Transformer架构，优先考量双链动态而非静态序列特征，从而解析局部催化基序（如g10–g11处的连续碱基配对）与远端结构扰动；并集成可解释性模块（显著性图谱、反事实分析），将相互作用模式与生化认知相关联，同时揭示位置特异性的切割规则。在四个PIWI向导数据集上的验证结果显示，PAIRNet在所有实验条件下始终位列前两名；相较于排名第二的方法，其在皮尔逊相关系数（Pearson Correlation Coefficient, PCC）上的相对提升最为显著：MILI样本提升34.7%，MIWI样本提升14.6%。至关重要的是，PAIRNet重现了关键的生物学原则——催化残基位点（g10–g11）的严格互补性与3'端错配的耐受性——这与PIWI动态的结构研究结果相一致。通过将生化精度与计算可扩展性相结合，PAIRNet为设计高特异性piRNA沉默工具奠定了路线图，同时加速了RNA引导的基因组防御的机制研究。