FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures

Zhou, Li; Wang, Xunxun; Yu, Shixiong; Tan, Ya-Lan; Tan, Zhi-Jie

doi:10.1016/j.bpj.2022.08.017

Cited by 10 publications

(18 citation statements)

References 86 publications

(99 reference statements)

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“…Among the available standalone codes, we only manage to run RNAJP (23). DeepFoldRNA (62), FebRNA (43) or RoseTTFoldNA (68) require the download of databases. Those databases could have more than 500Gb and thus be hardly usable for users.…”

Section: Resultsmentioning

confidence: 99%

“…Methods like RNABuilder (30) and ModeRNA (31) use one nucleotide per fragment, while FARNA/FARFAR (32) and FARFAR 2 (33) use three nucleotides per fragment. MC-Sym (34), RNAComposer (35), Vfold (36), VfoldLA (37), 3dRNA (38), Vfold Pipeline (39) and FebRNA (40) consider as base representation SSEs. The predicted structure can be refined to prevent clashes with energy minimization.…”

Section: Methodsmentioning

confidence: 99%

“…Among the remaining tools, unfortunately, many are hard to use or not working. Among the available standalone codes, we only manage to run RNAJP (22), while the other tools like Deep-FoldRNA (57), FebRNA (39) or RoseTTFoldNA (59) require the download of databases. Those databases could have more than 500Gb and thus be hardly usable for users.…”

Section: Benchmarked Toolsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Benchmarked Toolsmentioning

confidence: 99%

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

Bernard,

Postic,

Ghannay

et al. 2023

Preprint

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“…Fragment-based techniques have been employed for prediction of RNA 3D structures [e.g. Rosetta, SWM, FARFAR 10 / FARFAR2 11 , 3dRNA 12, 13, 14, 15 , FebRNA 16 ], RNA-RNA complex structures 17 , and protein-RNA complex structures 18 .…”

Section: Introductionmentioning

confidence: 99%

On the completeness of existing RNA fragment structures

Hong,

Zhan,

Zhou

2024

Preprint

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Success in protein structure prediction by the deep learning method AlphaFold 2 naturally gives arise the question if we can do the same for RNA structure prediction. One reason for the success in protein structure prediction is that the structural space of proteins at the fragment level has been nearly complete for many years. Here, we examined the completeness of RNA fragment structural space at dimeric, trimeric, tetrameric, and pentameric levels. We showed that the RNA structural space is not even complete at the di-nucleotide level, whereas the exponential increase of new structural fragments is observed at tetrameric and pentameric levels. Moreover, the number of backbone fragments found in RNA (2510) is far smaller than the number of backbone fragments found in proteins (6652). This further suggests that a structural space currently observed in RNA is far from complete, considering that the RNA backbone (6 torsion angles) has more degrees of freedom than the protein backbone (3 torsion angles with one nearly fixed). In addition, we found that the three-atom representation (one backbone atom C4' and two sidechain atoms C1' and N1) has the lowest number of structural fragments, suggesting it as the most "stable" structural frame for building up the entire RNA structure.

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“…For the RNA study, Zhao et al illustrated 3dRNA which utilized the smallest secondary structure elements to predict RNA tertiary structures automatically [ 10 ]. Zhou et al proposed FebRNA to build tertiary RNA structures through coarse-grained fragment assembly [ 11 ]. Traditional methods use knowledge-based statistical potential or force fields to evaluate RNA structures.…”

mentioning

confidence: 99%