Evidence map›Paper›PMID 41727101›Full record

ArticlebioRxiv : the preprint server for biology2026

A Large-Scale Cryo-EM RNA Motif Dataset and Benchmark for Machine Learning-Based Structure Modeling.

Chandramathi Murugadass, Hajira Rana, Brent M Znosko, Jie Hou, Dong Si

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Chandramathi MurugadassDepartment of Computer Science and Software Engineering, University of Washington Bothell.ORCID 0009-0007-7920-8961
Hajira RanaDepartment of Computer Science and Software Engineering, University of Washington Bothell.
Brent M ZnoskoDepartment of Chemistry, Saint Louis University.
Jie HouDepartment of Computational Mathematics, Science and Engineering, Michigan State University.ORCID 0000-0002-8584-5154
Dong SiDepartment of Computer Science and Software Engineering, University of Washington Bothell.ORCID 0000-0001-7039-2589

Funding

Understanding the Thermodynamics and Structure of RNA Secondary Structure MotifsR15GM085699 · NIGMS · SAINT LOUIS UNIVERSITY · PI ZNOSKO, BRENT · 2009 to 2025
$2.0M
Improving Artificial Intelligence Readiness of RNA Motif Data for Structure Analysis and ModelingR15GM155891 · NIGMS · SAINT LOUIS UNIVERSITY · PI HOU, JIE · 2024 to 2024
$467k
NIGMS NIH HHS R15 GM085699NIGMS NIH HHS R15 GM155891
6 · The paper itself

Abstract

Motivation: RNA molecules play critical roles in gene regulation, viral replication, and cellular control, with their functions tightly coupled to three-dimensional structure. Advances in cryogenic electron microscopy (cryo-EM) now enable RNA structure characterization across a broad resolution range. RNA secondary structural motifs, including hairpins, internal loops, and bulges, act as fundamental building blocks of RNA tertiary architecture and are key targets in RNA-focused therapeutic design. Despite this, most computational approaches for RNA structure prediction from cryo-EM density maps do not explicitly utilize secondary structural motifs as intermediate representations, largely due to the absence of large-scale, high-quality, and motif-resolved datasets suitable for machine learning. Results: Here, we present a large, open-source dataset containing over 125,000 motif-resolved cryo-EM density maps paired with corresponding atomic structures, spanning 25 classes of RNA secondary structural motifs. The dataset covers resolutions from 1.5 Å to 34.0 Å, encompassing both near-atomic and low-resolution density maps relevant to RNA modeling. Each motif instance includes a segmented cryo-EM density map represented as a standardized 3D voxel grid, with atomic-level motif annotations propagated to voxel-level labels for RNA backbone, ribose sugar, and nucleobase components. Segmentation quality is validated via cross-correlation analysis, demonstrating strong agreement between motif-level density maps and atomic reference models. To illustrate the dataset's utility, high-resolution maps (1.5-2.8 Å) were used to train a machine learning classifier that distinguished five motif classes with a specificity of 0.948. Availability and Implementation: Source code, implementation of the fully automated pipeline, and the benchmark datasets are publicly available at. GitHub: https://github.com/DrDongSi/3DEM-RNA-Motif-Dataset. Zenodo: https://zenodo.org/communities/3dem-rna-motif-dataset.

Indexed as

Classificationcryo-EMLabelMachine LearningMotifRNASecondary StructureStructure Modeling

Identifiers

PMID41727101
PMCPMC12918981

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.