Evidence map›Paper›PMID 39524454›Full record

ArticleCommunications in information and systems2024

smFRET-assisted RNA structure prediction.

Jun Li, Nils G Walter, Shi-Jie Chen

Abstract read
In one paragraph

Article in Communications in information and systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. FRET-guided selection of RNA 3D structures.Nucleic acids research · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jun LiDepartment of Physics, University of Missouri, Columbia, MO, USA.
Nils G WalterSingle Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry, University of Michigan, Ann Arbor, MI, USA.
Shi-Jie ChenDepartment of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.

Funding

The RNA nanomachines of the gene expression machinery dissected at the single molecule levelR35GM131922 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NILS G WALTER · 2019 to 2026
$6.9M
New methods for computational modeling of RNA structuresR35GM134919 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI SHI-JIE CHEN · 2020 to 2026
$3.3M
NIGMS NIH HHS R35 GM131922NIGMS NIH HHS R35 GM134919
6 · The paper itself

Abstract

Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful biophysical technique that utilizes the distance-dependent energy transfer between donor and acceptor dyes linked to individual molecules, providing insights into molecular conformational changes and interactions at the single-molecule level. Prior investigations leveraged smFRET to study the conformational dynamics of single truncated Ubc4 pre-mRNA molecules during splicing, yet these efforts did not prioritize structural modeling. In this study, we develop an smFRET-assisted RNA prediction method to predict the 2D and 3D structures of this pre-mRNA. To achieve this, we initiate the process by generating RNA structural ensembles through coarse-grained molecular dynamics (MD) simulations. Subsequently, inter-dye distances are calculated for these RNA structural ensembles by performing all-atom MD simulations of the dye groups. The ultimate determination of the 2D and 3D structures for the pre-mRNA is achieved by comparing the calculated inter-dye distances with experimental counterparts. Notably, our computational results demonstrate a significant alignment with experimental findings, which involve a conformational change at the 2D level.

Indexed as

coarse-grained MD simulationRNA structure predictionsmFRETUbc4 pre-mRNA

Identifiers

PMID39524454
PMCPMC11545564

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Registered trials

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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.