Evidence map›Paper›PMID 32949490›Full record

ArticleBiophysical journal2020

Modeling Loop Composition and Ion Concentration Effects in RNA Hairpin Folding Stability.

Chenhan Zhao, Dong Zhang, Yangwei Jiang, Shi-Jie Chen

Open access · bronzeAbstract read
In one paragraph

Article in Biophysical journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Computer-Assisted Design and Characterization of RNA Nanostructures.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  5. Article
  6. Article
  7. The Multiscale Future of RNA Modeling.Biophysical journal · 2020
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Chenhan ZhaoDepartment of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri.
Dong ZhangDepartment of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri.
Yangwei JiangDepartment of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri.
Shi-Jie ChenDepartment of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri. Electronic address: chenshi@missouri.edu.
University of Missouri · US

Funding

New methods for computational modeling of RNA structuresR35GM134919 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI SHI-JIE CHEN · 2020 to 2026
$3.3M
Statistical Mechanics of RNA FoldingR01GM063732 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI CHEN, SHI-JIE · 2003 to 2018
$3.2M
New computational tools for predicting ion effects in RNA structuresR01GM117059 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI CHEN, SHI-JIE · 2017 to 2020
$1.2M
NIGMS NIH HHS R01 GM063732NIGMS NIH HHS R01 GM117059NIGMS NIH HHS R35 GM134919
6 · The paper itself

Abstract

The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequence- and salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.

Indexed as

RNARNA FoldingMolecular Dynamics SimulationNucleic Acid ConformationRNA StabilityThermodynamicsRNA

Identifiers

PMID32949490
PMCPMC7568001
OpenAlexW3082405552

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.