Evidence map›Paper›PMID 34798137›Full record

ArticleBiophysical journal2022

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation.

Ya-Lan Tan, Xunxun Wang, Ya-Zhou Shi, Wenbing Zhang, Zhi-Jie Tan

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Article in Biophysical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

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26citing papers in PubMed
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3 · Its place in the literature

Who cites it

26 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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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ya-Lan TanResearch Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China; Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
Xunxun WangDepartment of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
Ya-Zhou ShiResearch Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China.
Wenbing ZhangDepartment of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China. Electronic address: wbzhang@whu.edu.cn.
Zhi-Jie TanDepartment of Physics and Key Laboratory of Artificial Micro- and 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 effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.

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ProteinsRNAProteinsRNA

Identifiers

PMID34798137
PMCPMC8758408

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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.