Diverse database and machine learning model to narrow the generalization gap in RNA structure prediction.

ABSTRACT: Understanding macromolecular structures of proteins and nucleic acids is critical for discerning their functions and biological roles. Advanced techniques-crystallography, nuclear magnetic resonance, and cryo-electron microscopy-have facilitated the determination of more than 180,000 protein structures, all cataloged in the Protein Data Bank. This comprehensive repository has been pivotal in developing deep learning algorithms for predicting protein structures directly from sequences. In contrast, RNA structure prediction has lagged and suffers from a scarcity of structural data. Here, we present the secondary structure models of 1098 primary microRNAs and 1456 human messenger RNA regions determined through chemical probing. We develop a deep learning architecture inspired from the Evoform

SUBMITTER: de Lajarte AA 

PROVIDER: S-EPMC12935039 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature