A riboswitch is a functional RNA region, typically found in the untranslated regions (UTRs) of mRNA, that regulates gene expression by undergoing structural changes in response to ligand binding or other stimuli. While the functional mechanisms of riboswitches have been extensively elucidated in bacteria, their identification and functional characterization in eukaryotes—which possess complex gene regulatory mechanisms—remain significant challenges in Biology. The comprehensive identification of riboswitches is crucial for deepening our understanding of gene regulatory networks. To address this, SwitchFinder was developed as a method to predict riboswitches from RNA sequences, utilizing thermodynamic calculations based on energy models of RNA secondary structure (Khoroshkin et al., 2024). However, this method is limited by its computational requirements; specifically, it is inapplicable to approximately 60% of the sequences in the Rfam riboswitch database, and challenges remain regarding its prediction accuracy. Therefore, the aim of this study was to construct a deep learning model capable of accurately predicting riboswitches across all available data, free from such limitations. Specifically, we designed a model based on a convolutional neural network that uses not only thermodynamic features of RNA secondary structure but also RNA sequence information and predicted secondary structure information as inputs. As a result, our method achieved an improvement in prediction accuracy (AUC) compared to SwitchFinder. Furthermore, we applied the model to human transcriptomes to identify candidate riboswitches and conducted experimental validation to assess their riboswitch activity.

Co-authors: Yusuke Hiki, Tsukasa Fukunaga