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. 2023 Sep 1;18(9):e0290907.
doi: 10.1371/journal.pone.0290907. eCollection 2023.

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models

Mandar Kulkarni  1 , Jayaraman Thangappan  1 , Indrajit Deb  1 , Sangwook Wu  1   2
Affiliations

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models

Mandar Kulkarni et al. PLoS One. .

Abstract

RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with different topologies to understand the effects of different 2D structure predictions on the accuracy of 3D modeling. The current study explores the blind challenge of 2D to 3D conversions and highlights the performances of de novo RNA 3D modeling from their predicted 2D structure constraints. Our results show that conformational sampling-based methods such as SimRNA and IsRNA1 depend less on 2D accuracy, whereas motif-based methods account for 2D evidence. Our observations illustrate the disparities in available 3D and 2D prediction methods and may further offer insights into developing topology-specific or family-specific RNA structure prediction pipelines.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Distributions of the PPV, sensitivity, and F1-score values for each of the seven RNA candidates under study.
The predicted 2D structures were compared to their corresponding native structures. These metrics range from 0 to 1, and a higher value indicates better prediction. Outliers are shown as diamond shape points.
Fig 2
Fig 2
(a) Distributions of the RMSD, INF_wc, INF_stack values, (b) method-specific RMSD values for high, medium, and low F1-score groups.
Fig 3
Fig 3
RMSD, deformation index, INF parameters, MCQ, and LCS-TA values for F1-scores greater than or equal to 0.95 (left) and less than 0.95 (right). The color codes are IsRNA1: green, RNAComposer: orange, and SimRNA: violet. Median values are indicated by white dots.
Fig 4
Fig 4. Pair plot distribution of F1-score values and RNA 3D accuracy parameters RMSD, INF_all, INF_wc, MCQ, and LCS-TA for RNAComposer (blue), SimRNA (orange), and IsRNA1 (green) methods.
Fig 5
Fig 5
RMSD values of all seven PDBs determined with (a) IsRNA1 simulations, (b) the RNAComposer web server, and (c) SimRNA simulations with 2D structural constraints based on various methods (x-axis).
Fig 6
Fig 6
Predicted 3D structures (blue) aligned with the native 3D structure (green) for IsRNA1 (left), RNAComposer (middle), and SimRNA (right) with native 2D structural constraints for Group I PDBs (5LYU, 6TB7, 7LYJ). The RMSDs are mentioned in parentheses.
Fig 7
Fig 7
Predicted 3D structures (blue) aligned with native 3D structure (green) for IsRNA1 (left), RNAComposer (middle), and SimRNA (right) with native 2D structural constraints for group II PDBs (5NWQ, 4ENC, 6P2H, 3OWZ (chain A)). The RMSDs are given in parentheses.
Fig 8
Fig 8
Heavy atom RMSD (top) and backbone RMSD (bottom) to the native 4ENC structure with AMBER OL3 and different water models (TIP3P (black), SPC/E (red), and OPC (green)).
Fig 9
Fig 9. RMSD values of repeated SimRNA simulations with native 2D structural constraints.
See this image and copyright information in PMC

References

    1. Mathews DH. How to benchmark RNA secondary structure prediction accuracy. Methods. 2019;162–163: 60–67. doi: 10.1016/j.ymeth.2019.04.003 - DOI - PMC - PubMed
    1. Treiber Daniel K and Williamson James R. Beyond kinetic traps in RNA folding. Curr Opin Struct Biol. 2001; 309–314. doi: 10.1016/s0959-440x(00)00206-2 - DOI - PubMed
    1. Treiber DK, Williamson JR. Exposing the kinetic traps in RNA folding. Curr Opin Struct Biol. 1999;9: 339–345. doi: 10.1016/S0959-440X(99)80045-1 - DOI - PubMed
    1. Orland H, Zee A. RNA folding and large N matrix theory. Nucl Phys B. 2002;620: 456–476. 10.1016/S0550-3213(01)00522-3 - DOI
    1. Brion P, Westhof E. Hierarchy and dynamics of rna folding. Annu Rev Biophys Biomol Struct. 1997;26: 113–137. doi: 10.1146/annurev.biophys.26.1.113 - DOI - PubMed

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