Evidence map›Paper›PMID 38542881›Full record

ArticleMolecules (Basel, Switzerland)2024

ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures.

Ya-Zhou Shi, Hao Wu, Sha-Sha Li, Hui-Zhen Li, Ben-Gong Zhang, Ya-Lan Tan

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. RNA Structure: Past, Future, and Gene Therapy Applications.International journal of molecular sciences · 2024
    Review
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

6 authors.

Ya-Zhou ShiResearch Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China.ORCID 0000-0003-2579-3134
Hao WuResearch Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China.
Sha-Sha LiResearch Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China.
Hui-Zhen LiResearch Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China.
Ben-Gong ZhangResearch Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China.ORCID 0000-0002-2766-6502
Ya-Lan TanResearch Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China.

Funding

Department of Education of Hubei Province Q20221705National Natural Science Foundation of China 12205223National Natural Science Foundation of China 12371500
6 · The paper itself

Abstract

RNAs play crucial roles in various essential biological functions, including catalysis and gene regulation. Despite the widespread use of coarse-grained (CG) models/simulations to study RNA 3D structures and dynamics, their direct application is challenging due to the lack of atomic detail. Therefore, the reconstruction of full atomic structures is desirable. In this study, we introduced a straightforward method called ABC2A for reconstructing all-atom structures from RNA CG models. ABC2A utilizes diverse nucleotide fragments from known structures to assemble full atomic structures based on the CG atoms. The diversification of assembly fragments beyond standard A-form ones, commonly used in other programs, combined with a highly simplified structure refinement process, ensures that ABC2A achieves both high accuracy and rapid speed. Tests on a recent large dataset of 361 RNA experimental structures (30-692 nt) indicate that ABC2A can reconstruct full atomic structures from three-bead CG models with a mean RMSD of ~0.34 Å from experimental structures and an average runtime of ~0.5 s (maximum runtime < 2.5 s). Compared to the state-of-the-art Arena, ABC2A achieves a ~25% improvement in accuracy and is five times faster in speed.

Indexed as

Molecular Dynamics SimulationRNANucleotidesNucleotidesRNAcoarse-grained modelfull atomic structure reconstructionRNA 3D structure

Identifiers

PMID38542881
PMCPMC10974898

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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.