Evidence map›Paper›PMID 24568646›Full record

ArticleJournal of the American Chemical Society2014

Complex RNA folding kinetics revealed by single-molecule FRET and hidden Markov models.

Bettina G Keller, Andrei Kobitski, Andres Jäschke, G Ulrich Nienhaus, Frank Noé

Abstract read
In one paragraph

Article in Journal of the American Chemical Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

  1. Article
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  8. Theory and Analysis of Single-Molecule FRET Experiments.Methods in molecular biology (Clifton, N.J.) · 2022
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  10. Review
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  15. Dynamic graphical models of molecular kinetics.Proceedings of the National Academy of Sciences of the United States of America · 2019
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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

5 authors.

Bettina G KellerFreie Universität Berlin , Institute of Chemistry and Biochemistry, Takustr. 3, 14195 Berlin, Germany.
Andrei Kobitski
Andres Jäschke
G Ulrich Nienhaus
Frank Noé

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have developed a hidden Markov model and optimization procedure for photon-based single-molecule FRET data, which takes into account the trace-dependent background intensities. This analysis technique reveals an unprecedented amount of detail in the folding kinetics of the Diels-Alderase ribozyme. We find a multitude of extended (low-FRET) and compact (high-FRET) states. Five states were consistently and independently identified in two FRET constructs and at three Mg(2+) concentrations. Structures generally tend to become more compact upon addition of Mg(2+). Some compact structures are observed to significantly depend on Mg(2+) concentration, suggesting a tertiary fold stabilized by Mg(2+) ions. One compact structure was observed to be Mg(2+)-independent, consistent with stabilization by tertiary Watson-Crick base pairing found in the folded Diels-Alderase structure. A hierarchy of time scales was discovered, including dynamics of 10 ms or faster, likely due to tertiary structure fluctuations, and slow dynamics on the seconds time scale, presumably associated with significant changes in secondary structure. The folding pathways proceed through a series of intermediate secondary structures. There exist both compact pathways and more complex ones, which display tertiary unfolding, then secondary refolding, and, subsequently, again tertiary refolding.

Indexed as

Fluorescence Resonance Energy TransferMarkov ChainsModels, MolecularRNA FoldingBase SequenceKineticsNucleic Acid ConformationRNA, CatalyticRNA, Catalytic

Identifiers

PMID24568646
PMCPMC3977575

What OpenQuestion holds

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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.