Evidence map›Paper›PMID 25712091›Full record

ArticleNucleic acids research2015

3dRNAscore: a distance and torsion angle dependent evaluation function of 3D RNA structures.

Jian Wang, Yunjie Zhao, Chunyan Zhu, Yi Xiao

Abstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

61 citing papers in PubMed.

  1. Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026
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  3. The long non-coding RNAFrontiers in nutrition · 2026
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  4. On the Completeness of Existing RNA Fragment Structures.Genomics, proteomics & bioinformatics · 2025
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  7. Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  11. Review
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  13. miRVim: Three-dimensional miRNA Structure Database.MicroRNA (Shariqah, United Arab Emirates) · 2025
    Article
  14. Article
  15. Article
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  17. Article
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  20. Article

1 more citing papers are in PubMed but not listed here.

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

4 authors.

Jian WangBiomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
Yunjie ZhaoBiomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
Chunyan ZhuBiomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
Yi XiaoBiomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China yxiao@mail.hust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.

Indexed as

Models, MolecularData Interpretation, StatisticalNucleic Acid ConformationRNATorsion, MechanicalRNA

Identifiers

PMID25712091
PMCPMC4446410

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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.