Advances in RNA 3D Structure Prediction

Ou, Xiujuan; Zhang, Yi; Xiong, Yiduo; Xiao, Yi

doi:10.1021/acs.jcim.2c00939

Cited by 18 publications

(16 citation statements)

References 84 publications

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“…The goal of ABC2A is to rapidly reconstruct the full atomic structure from a CG model using full atomic nucleotide fragments with diverse configurations in the PDB. 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%

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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Section: Number Of Fragmentsmentioning

confidence: 99%

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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“…3dRNA/DNA is still one of the most accurate single‐sequence‐based prediction methods; its prediction accuracy is about 3 Å for RNA <50 nt and ∼6 Å for RNA of 50‐100 nt. More details of these prediction methods can be seen in our review paper (Ou et al., 2022).…”

Section: Commentarymentioning

confidence: 99%

Using 3dRNA/DNA for RNA and DNA 3D Structure Prediction and Evaluation

et al. 2023

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This article describes how to use 3dRNA/DNA to predict RNA and DNA 3D structures from their sequences and secondary (2D) structures. The predicted RNA and DNA 3D structures can be used to predict or understand their functions and can also be used to find the interactions between the RNA/DNA and other molecules. © 2023 Wiley Periodicals LLC. Basic Protocol: RNA/DNA 3D structure prediction using 3dRNA/DNA web server Alternate Protocol: Using 3dRNA/DNA and 3dRNAscore locally Support Protocol: Installing NSP

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“…Various computational methods have been developed for RNA structure prediction [14,17]. They can be either ab initio, which try to predict the structure from scratch or template-based, which predict the structure based on information from already known structures.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, RNA structures are inherently more challenging to resolve due to their dynamic nature, where they can adopt multiple conformations under different cellular conditions [12,13]. Given the challenges and limitations of experimental methods, computational approaches have emerged as essential tools in predicting RNA’s 3D structure [14]. These computational methods leverage existing experimental data, principles of physics, and statistical analyses combined with machine learning to generate plausible models of RNA structures rapidly and cost-effectively [15].…”

Section: Introductionmentioning

confidence: 99%

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction

Bahai,

Kwoh,

et al. 2024

Preprint

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The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, have shown some promise for RNA structure prediction as well but face unique challenges. This study systematically benchmarks state-of-the-art deep learning methods for RNA structure prediction across diverse datasets. Our aim is to identify factors influencing performance variation, such as RNA family diversity, sequence length, RNA type, multiple sequence alignment (MSA) quality, and deep learning model architecture. We show that generally ML-based methods perform much better than non-ML methods on most RNA targets, although the performance difference is not substantial when working with unseen novel or synthetic RNAs. The quality of the MSA and secondary structure prediction both play an important role and most methods are not able to predict non-Watson-Crick pairs in the RNAs. Overall, DeepFoldRNA has the best prediction followed by DRFold as the second best method. Finally, we also suggest possible mitigations to improve the quality of the prediction for future method development.

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Advances in RNA 3D Structure Prediction

Cited by 18 publications

References 84 publications

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

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

Using 3dRNA/DNA for RNA and DNA 3D Structure Prediction and Evaluation

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction

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