# PAIRNet: Predicting PIWI cleavage specificity via position-aware RNA interaction modeling

## TL;DR

PAIRNet is a deep learning model that predicts how PIWI proteins cleave RNA targets, improving accuracy and uncovering key biological rules.

## Contribution

PAIRNet introduces a novel interaction-centric model that outperforms existing methods by up to 34.7% and identifies position-specific cleavage rules.

## Key findings

- PAIRNet achieves significant improvements in prediction accuracy over existing methods, with 34.7% improvement for MILI and 14.6% for MIWI.
- The model identifies critical cleavage rules, such as strict pairing at catalytic sites and tolerance for 3’ mismatches.
- PAIRNet aligns with structural studies of PIWI dynamics and enables scalable exploration of RNA-guided genome defense mechanisms.

## Abstract

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.

Small RNA systems like miRNA and siRNA—honored by Nobel Prizes—play vital roles in biology. As another key member, piRNA works with PIWI proteins to defend genomes by cutting viruses and mobile genetic elements. However, unlike miRNA and siRNA, piRNA’s targeting rules are far more complex, limiting its therapeutic potential. While recent studies use labor-intensive experiments like Cleave-N’-Seq to decode these rules, reproducing such work requires simulating biochemical reactions across multiple time points—a slow and technically challenging process. To solve this, we developed a computational method that focuses on how piRNA and target RNAs physically interact, rather than just analyzing sequences. Traditional approaches, which stitch sequences together or count generic patterns (e.g., K-mers), achieved low accuracy. In contrast, our interaction-centric model—designed to mirror natural PIWI behavior—not only improved predictions by up to 34.7% over conventional tools, but also identified critical rules, such as strict pairing at catalytic sites and flexibility elsewhere. This success demonstrates that modeling biological systems should start with the problem’s essence, not just sequences. By aligning computation with nature’s logic, we can accelerate piRNA-based therapies and inspire smarter tools for genome engineering.