Genomic foundation models reveal RNA-binding proteins regulating translation and mRNA degradation during Drosophila development

The regulation of mRNA decay and translation is crucial for cellular function and development; however, the complex interplay of RNA-binding proteins (RBPs) regulating these processes remains incompletely understood. Recent advances in genomic foundation models present new opportunities for decoding the regulatory grammar embedded within mRNA untranslated regions (UTRs). Here, we leverage explainable artificial intelligence to systematically identify RBP motifs that influence translation and mRNA decay during Drosophila melanogaster development. We extended the training of GENA-LM Fly, a genomic foundation model, on all 5’ and 3’ UTR pairs from the D. melanogaster genome. We separately fine-tuned it using ribosome density (RD) and mRNA decay (half-life) data from the maternal-to-zygotic transition (MZT). Using SHapley Additive exPlanations (SHAP) analysis, we identified the sequence regions most influential for prediction and performed motif enrichment analysis to discover associated RBP binding sites. We identified 42 unique RBPs associated with increased (n=23; e.g., RNP4F, Mxt) or decreased (n=19; e.g., ARET/Bruno, ROX8, SXL) RD and 18 unique RBPs associated with increased (n=6) and decreased (n=12; e.g., CNOT4, RBP9, ROX8) mRNA half-life. Feature ablation and shuffling experiments revealed the contributions of different sequence components to model performance. Our approach significantly outperformed naïve highversus-low RD comparisons, demonstrating the power of model explainability in biological discovery. This study demonstrates that genomic foundation models, when combined with explainability methods, can discover meaningful biology even without drastically improving the underlying prediction accuracy. The identified RBP motifs provide new insights into post-transcriptional regulatory elements that govern RD and decay during early development.