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

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 hybri

SUBMITTER: Zeng L 

PROVIDER: S-EPMC12919788 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature