Evidence map›Paper›PMID 40653707›Full record

ArticleBiophysical journal2025

rsRNASP1: A distance- and dihedral-dependent statistical potential for RNA 3D structure evaluation.

En Lou, Chen-Chen Zheng, Shixiong Yu, Ya-Lan Tan, Zhi-Jie Tan

Abstract read
In one paragraph

Article in Biophysical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

En LouDepartment of Physics and Key Laboratory of Artificial Micro & Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
Chen-Chen ZhengDepartment of Physics and Key Laboratory of Artificial Micro & Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
Shixiong YuDepartment of Physics and Key Laboratory of Artificial Micro & Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
Ya-Lan TanSchool of Bioengineering and Health & Research Center of Nonlinear Science, Wuhan Textile University, Wuhan, China. Electronic address: yltan@wtu.edu.cn.
Zhi-Jie TanDepartment of Physics and Key Laboratory of Artificial Micro & Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China. Electronic address: zjtan@whu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knowledge-based statistical potentials have been shown to be rather important for RNA three-dimensional (3D) structure prediction and evaluation. Recently, several statistical potentials/scoring functions have been developed for RNA 3D structure evaluation, but their performances are still at an unsatisfactory level for the data sets from various 3D structure prediction methods. In this work, we developed an all-atom distance- and torsion-angle-dependent statistical potential with residue separation for RNA 3D structure evaluation, named as rsRNASP1, by considering torsion angles for the backbone, sugar ring, and base to involve local structure features. The extensive examinations against available RNA test data sets show that rsRNASP1 has an overall higher performance than existing top statistical potentials/scoring functions in identifying native/near-native structures and ranking the decoy structures. Especially, rsRNASP1 shows an apparently improved performance on a new data set from the CASP15 competition.

Indexed as

Nucleic Acid ConformationRNAModels, MolecularRNA

Identifiers

PMID40653707
PMCPMC12414698

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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.