Enhancing Pirna Cleavage Prediction with Pir-Dann: Integrating in Vitro and in Vivo Insights

PIWI-interacting RNAs (piRNAs) are critical genome guardians that guide PIWI proteins to cleave trans-posable element transcripts. Predicting these cleavage events is vital for understanding gene regulation but is challenged by the complex, “relaxed” rules of piRNA targeting and the noisy cellular context of in vivo data. Previous models, trained solely on in vivo data, struggle with high noise, non-functional binding events, and binary outputs that fail to capture the quantitative biophysics of piRNA-target interactions. Recognizing that precise, quantitative in vitro data directly quantifies biophysical parameters of cleavage, we aimed to leverage this information to overcome the noise and complexity inherent in in vivo observations. To achieve this, we developed piR-DANN, a deep learning framework based on a novel, biologically-informed adversarial domain adaptation strategy. Our core innovation moves beyond conventional approaches by providing the domain classifier with structural and sequence determinants of targeting summarizing known biological rules, in addition to the deep features learned by the model. This asymmetric design compels the feature extractor to learn the fundamental, domain-invariant principles of piRNA targeting. piR-DANN outperforms existing benchmarks predictive performance, with AUROC scores of 94.7% and 99.7% on two independent test sets. Furthermore, counterfactual analysis reveals a positional importance map concordant with PIWI catalytic core constraints, validating its extraction of biological signal from noise. By integrating heterogeneous data into an accurate, interpretable framework, our work explores piRNA biology and proposes a generalizable approach for deciphering gene regulatory systems.