RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools

By Marcin Magnus, Maciej Antczak, Tomasz Zok, Jakub Wiedemann, Piotr Lukasiak, Yang Cao, Janusz M. Bujnicki, Eric Westhof, Marta Szachniuk, Zhichao Miao. 2019

76 citations recorded by the source corpus.

Abstract Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of RNA-Puzzles, a community-wide effort on the assessment of blind prediction of RNA tertiary structures. The RNA-Puzzles contest has shown, among others, that the development and validation of computational methods for RNA fold prediction strongly depend on the benchmark datasets and the structure comparison algorithms. Yet, there has been no systematic benchmark set or decoy structures available for the 3D structure prediction of RNA, hindering the standardization of comparative tests in the modeling of RNA structure. Furthermore, there has not been a unified set of tools that allows deep and complete RNA structure analysis, and at the same time, that is easy to use. Here, we present RNA-Puzzles toolkit, a computational resource including (i) decoy sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and standardized datasets), (ii) 3D structure normalization, analysis, manipulation, visualization tools (RNA_format, RNA_normalizer, rna-tools) and (iii) 3D structure comparison metric tools (RNAQUA, MCQ4Structures). This resource provides a full list of computational tools as well as a standard RNA 3D structure prediction assessment protocol for the community.

Subfields: Molecular Biology

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Citations in the corpus

Assessment of three-dimensional RNA structure prediction in CASP15 (2023, positive): “We selected the following tools for our ranking scheme: (1) US-align, 31 which was used to compute TM-score through a heuristic alignment approach improving on the original RNA-align 29 ; (2) Local – Global Alignment 32 which yielded GDT_TS, the average percentage of aligned C4 0 atoms (rather than ”
Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, positive): “We first used the results from the RNA-Puzzles 26–30 competition, where the submissions were produced and optimized by human knowledge or computational methods.”
New prediction categories in CASP15 (2023, methodology): “We additionally consider an RNA-specific measure, Interaction Network Fidelity (INF), 13,20 which evaluates different types of RNA interactions in models.”
RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers (2020, background): “As discussed before (Miao and Westhof 2017), secondary structure (the knowledge of the helical Watson–Crick pairs) alone is far from sufficient to model RNA structures without prediction of non-WC base pairs.”
RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints (2023, methodology): “Finally, we used the RNA-Puzzles toolkit 20 for formatting the files for submission.”
FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, unknown): “We ran FARFAR2 for each of the 21 problems for which an experimental structure is now available (Magnus et al., 2019), again using procedures to ensure that fragments from these structures or their homologs could not be sampled, providing ‘‘like-blind’’ modeling conditions (see Fragment Homology…”
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “…and deformation index (DI) 28 scores of the models generated by DRfold and the control methods, which were calculated using the RNA-Puzzles toolkit 29 In Table S2, we further list the mean of circular quantity (MCQ) 30 and Handedness scores of the DRfold models compared to the ﬁ ve control…”