MicroRNAs (miRNAs) are processed from structured precursors and subsequently loaded into Argonaute proteins to repress target mRNAs. Yet generalized computational frameworks capable of decoding mature miRNAs from precursor context remain limited, constraining both cross-species annotation and rational artificial miRNA design. Here, we present miRstring, a biogenesis-aware RNA language framework that decodes the four boundaries defining the miRNA/miRNA duplex. Trained on 77,708 miRNA precursors spanning 414 species, miRstring outperforms existing methods under family- and species-held-out evaluations and accurately identifies the first nucleotide of mature miRNAs. Importantly, its attention mechanism highlights the miRNA/ miRNA* boundary sites cleaved by endonucleases, indicating that the model captures biologically meaningful features. Furthermore, we employed miRstring to design optimal pre-miRNA scaffolds for artificial miRNAs and validated its efficacy in repressing target mRNAs. Taken together, miRstring establishes a scalable route from cross-species mature-miRNA annotation to predictive design, enabling artificial intelligence-driven miRNA engineering and extending computational miRNA analysis toward broadly applicable small-RNA biotechnology.
Peng, R., Li, X., fang, t., Yu, X.
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