A large-scale cryo-EM RNA motif dataset and benchmark for machine learning-based structure modeling.

motivationRNA functions in gene regulation, viral replication, and cellular control are tightly coupled to three-dimensional structure and local conformational features. Cryogenic electron microscopy (cryo-EM) now enables RNA structure characterization across a broad resolution range, but full maps are large, heterogeneous, and variable in local resolution. RNA secondary structural motifs, including hairpins, internal loops, and bulges, provide recurring local units for interpreting RNA density, comparing structures, and developing machine-learning models. Existing cryo-EM-based methods generally focus on complete maps, chains, residues, or atomic model construction rather than motif-level representations, partly because large-scale motif-resolved cryo-EM datasets remain limited.

resultsWe present an open-source dataset of more than 100,000 motif-resolved cryo-EM density segments paired with atomic structures, spanning 25 RNA secondary structural motif classes and resolutions from 1.5 Å to 34.0 Å. Each motif is represented as a standardized 3D voxel grid with voxel-level labels for RNA backbone, ribose sugar, and nucleobase components. Motif-level map-model agreement was evaluated using masked cross-correlation (CC AVAILABILITY AND IMPLEMENTATION: Source code, pipeline implementation, benchmark datasets, and an interactive web application are available at GitHub (https://github.com/DrDongSi/3DEM-RNA-Motif-Dataset), Zenodo (https://zenodo.org/communities/3dem-rna-motif-dataset), and Hugging Face Spaces (https://huggingface.co/spaces/houlab/arsma-cryoem).