Quantifying RNA Folding Equilibria from Unclassified Cryo-EM Particle Images
- 1Flatiron Institute
Abstract
Functional RNA structures are typically far more dynamic than most proteins solved by cryo-electron microscopy (cryo-EM). Flexible junctions and weak tertiary contacts produce broad conformational ensembles, and cryo-EM particle classification analyses discard unaligned particles, which inherently contain the information needed to quantify such broad ensembles. Here, we infer compositional and conformational distributions from unclassified particle images by Bayesian ensemble reweighting. We tested our approach on the P4-P6 domain of the Tetrahymena thermophila group I intron, a classic model system of Mg2+-dependent RNA folding. While stable monomeric and dimeric states yielded high-resolution reconstructions, analyses of abundant, flexible conformations required Bayesian ensemble inference. Our approach quantitatively captured the Mg2+-dependent folding transition, indicating that cryo-EM can report the abundance of coexisting RNA states in agreement with bulk solution measurements. These results showcase how quantitative studies of RNA folding can be achieved by combining cryo-EM measurements with statistical ensemble-inference tools.
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BibTeX
@misc{maji2026quantifying,
title = {{Quantifying RNA Folding Equilibria from Unclassified Cryo-EM Particle Images}},
author = {D. Maji and L. Dingeldein and M. Li and M. A. Astore and L. Evans and W. S. Tang and J. Rogal and A. Rangan and R. Covino and A. de Marco and S. L. Bonilla and P. Cossio},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.11.758367},
url = {https://www.biorxiv.org/content/10.64898/2026.10.11.758367v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Quantifying RNA Folding Equilibria from Unclassified Cryo-EM Particle Images
AU - D. Maji
AU - L. Dingeldein
AU - M. Li
AU - M. A. Astore
AU - L. Evans
AU - W. S. Tang
AU - J. Rogal
AU - A. Rangan
AU - R. Covino
AU - A. de Marco
AU - S. L. Bonilla
AU - P. Cossio
PY - 2026
DA - 2026/10/11
PB - bioRxiv
DO - 10.64898/2026.10.11.758367
UR - https://www.biorxiv.org/content/10.64898/2026.10.11.758367v1
AB - Functional RNA structures are typically far more dynamic than most proteins solved by cryo-electron microscopy (cryo-EM). Flexible junctions and weak tertiary contacts produce broad conformational ensembles, and cryo-EM particle classification analyses discard unaligned particles, which inherently contain the information needed to quantify such broad ensembles. Here, we infer compositional and conformational distributions from unclassified particle images by Bayesian ensemble reweighting. We tested our approach on the P4-P6 domain of the Tetrahymena thermophila group I intron, a classic model system of Mg2+-dependent RNA folding. While stable monomeric and dimeric states yielded high-resolution reconstructions, analyses of abundant, flexible conformations required Bayesian ensemble inference. Our approach quantitatively captured the Mg2+-dependent folding transition, indicating that cryo-EM can report the abundance of coexisting RNA states in agreement with bulk solution measurements. These results showcase how quantitative studies of RNA folding can be achieved by combining cryo-EM measurements with statistical ensemble-inference tools.
ER -