Arena: Rapid and Accurate Reconstruction of Full Atomic RNA Structures From Coarse-grained Models

Perry, Zion R.; Pyle, Anna Marie; Zhang, Chengxin

doi:10.1016/j.jmb.2023.168210

Cited by 6 publications

(15 citation statements)

References 20 publications

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“…Due to these stringent requirements, DSSR-assigned secondary structures might have missing positions compared to the input RNA. To ensure consistency between DSSR input and output, we utilize Arena [46] to fill in missing atoms and rectify unphysical nucleobase conformations for all RNA chains before we execute the DSSR assignment. For an RNA-RNA interaction involving two RNA chains, we assign secondary structures to both the individual RNAs and the RNA pair.…”

Section: Methodsmentioning

confidence: 99%

FURNA: a database for function annotations of RNA structures

Zhang,

Freddolino

2023

Preprint

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Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by non-coding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions ofRNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand binding sites, RNA families, protein binding motifs, and cross-references to related databases. FURNA is available athttps://seq2fun.dcmb.med.umich.edu/furna/to enable quick discovery of RNA functions from their structures and sequences.

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Section: Methodsmentioning

confidence: 99%

FURNA: a database for function annotations of RNA structures

Zhang,

Freddolino

2023

Preprint

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“…To investigate the influence of the number of fragments used for reconstructing individual nucleotides on accuracy and time, we reconstructed full atomic structures from three-bead CG models using varying numbers of fragments (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) for the test set used by Perry et al, which includes 361 RNA single-stranded chains with lengths from 30 to 692 nt; see Materials and Methods or Refs. [42,45].…”

Section: Number Of Fragmentsmentioning

confidence: 99%

“…Figure 3 also shows the results from ABC2A using only the standard A-form fragment for each nucleotide (i.e., ABC2A-1). PDBFixer in OpenMM [47], and Rosetta rna_thread [48]) in both speed and accuracy [42], we did not compare ABC2A with methods other than Arena in this paper. Figure 3 also shows the results from ABC2A using only the standard A-form fragment for each nucleotide (i.e., ABC2A-1).…”

Section: Performance Of Abc2amentioning

confidence: 99%

“…Nevertheless, the tool is specifically designed for the NARES-2P model (a CG model with two centers of interaction per repeating unit) [44] and lacks general applicability. Very recently, Perry et al introduced Arena (https://github.com/pylelab/Arena, accessed on 25 January 2024), a highly accurate and user-friendly tool, capable of generating full atomic structures for any CG RNA model with a minimum of one atom per nucleotide [42]. Like NARall, Arena achieves reconstruction by superimposing A-form standard fragments onto a CG model, aligning with the positions of CG atoms.…”

Section: Introductionmentioning

confidence: 99%

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

Shi,

Wu,

et al. 2024

Molecules

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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.

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“…These predicted frame vectors and geometry restraints are aggregated to a potential for structure reconstruction. The final step includes all-atom reconstruction and refinement using Arena (75) and OpenMM (76).…”

Section: Deep Learning Approachesmentioning

confidence: 99%

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

Bernard,

Postic,

Ghannay

et al. 2023

Preprint

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RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as eitherab initioor template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but the adaptation to RNA 3D structure prediction remains at stake. In this work, we give a brief review of theab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine approaches using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked models, freely available on the EvryRNA platform:https://evryrna.ibisc.univ-evry.fr.

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Arena: Rapid and Accurate Reconstruction of Full Atomic RNA Structures From Coarse-grained Models

Cited by 6 publications

References 20 publications

FURNA: a database for function annotations of RNA structures

FURNA: a database for function annotations of RNA structures

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

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

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