ArticleNucleic acids research2024
Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Decoding RNA Structural Ensembles: Energy Landscape Exploration of the TAR Stemloop.Journal of chemical theory and computation · 2026Article
- Unknotting RNA: A method to resolve computational artifacts.PLoS computational biology · 2025Article
- Predicting RNA structure and dynamics with deep learning and solution scattering.Biophysical journal · 2025Article
- Predicting RNA Structure and Dynamics with Deep Learning and Solution Scattering.bioRxiv : the preprint server for biology · 2024Article
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4 authors.
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Abstract
The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.
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