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. 2024 May 14;6(2):lqae048.
doi: 10.1093/nargab/lqae048. eCollection 2024 Jun.

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

Clément Bernard  1   2 , Guillaume Postic  1 , Sahar Ghannay  2 , Fariza Tahi  1
Affiliations

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

Clément Bernard et al. NAR Genom Bioinform. .

Abstract

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 either ab initio or 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 their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.

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Figures

Figure 1.
Figure 1.
Overview of the state-of-the-art methods for predicting RNA 3D structures. The different inputs are either raw sequence, secondary structure, tertiary structure or multiple sequence alignment (MSA). Dashed methods are preprint works.
Figure 2.
Figure 2.
The normalized mean of metrics for each of the benchmarked methods on the different datasets. The pooled test set is named ‘All’. For each metric, we normalised by the min–max to ensure values are between 0 and 1, and we reverse the order for descending metrics (RMSD, εRMSD, P-VALUE and MCQ). For a given metric, a model with a score near 1 means it has the best score compared to the other models.
Figure 3.
Figure 3.
Predicted structures (in blue) for RNAPuzzle 03 (rp03) (id: 3OWZ, length: 84 nucleotides) compared to native structure (in green) using state-of-the-art methods. (A) MC-Sym. (B) Vfold3D. (C) RNAComposer. (D) SimRNA. (E) 3dRNA. (F) IsRNA1. (G) RhoFold. (H) trRosettaRNA. (I) Vfold-Pipeline. (J) RNAJP. Alignment was done using CHIMERA (100) and Needleman–Wunsh algorithm (101).
Figure 4.
Figure 4.
Approximate time for computation of RNA-Puzzles structures. The minimum time is for an RNA of 27 nucleotides, while the maximum time is computed for an RNA of 188 nucleotides. The computation time is an approximation, as it was run on web servers and might be slowed down by other pending jobs. The time reported for RhoFold is with the relaxation (which is slower than the raw prediction). RNAJP computation time is computed locally with a simulation time set to 50 × 106 steps. IsRNA1 maximum time is around 15 hours, and SimRNA maximum computation time is around two days.
Figure 5.
Figure 5.
Screenshot of the State-of-the-RNArt dashboard. (A) The user can choose the RNA (or challenge) with its native structure to process with the different RNA 3D structure prediction tools. It is associated with the predicted metrics normalized by the maximum value for each metric for the given RNA challenge. (B) 3D visualizations of the different predictions of the benchmarked models. The native structure is coloured in orange, while the predictions are in blue. The predictions are superimposed with the native structure for visualization using the US-align (102) tool. The associated metrics are also shown on top of the structures.
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