Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions

Carrascoza, Francisco; Antczak, Maciej; Miao, Zhichao; Westhof, Éric; Szachniuk, Marta

doi:10.1261/rna.078685.121

Cited by 20 publications

(16 citation statements)

References 54 publications

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“…RNA 3D structure evaluation was conducted using rna-tools [ 62 , 63 ]. RCSB MAXIT was applied to evaluate the stereochemistry of the 3D structures submitted [ 64 , 65 ]. The RNAspider pipeline [ 7 , 66 ] was applied to identify and classify entanglements of structural elements, that is, spatial arrangements involving two structural elements, where at least one punctures the other.…”

Section: Methodsmentioning

confidence: 99%

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models

Gumna

Antczak

Adamiak

et al. 2022

IJMS

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RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5′- and 3′-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

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Section: Methodsmentioning

confidence: 99%

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models

Gumna

Antczak

Adamiak

et al. 2022

IJMS

Self Cite

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“…Since the present version of FebRNA was developed for building 3D structures of single-stranded RNAs, in the following, FebRNA was examined against the single-stranded RNAs in the RNA-Puzzles dataset [65-68]. These single-stranded RNAs were divided into two categories: those without nucleotide loss and those with single nucleotide loss, i.e., there is no ‘&’ for the former and there is ‘&’ for the latter in the dot-bracket form of their secondary structures.…”

Section: Resultsmentioning

confidence: 99%

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

Zhou

Wang

et al. 2022

Preprint

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Knowledge of RNA 3-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, high accuracy of predicted RNA 3D structures is still limited to the RNAs with short length or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of CG fragment ensembles, building CG 3D structure ensemble through fragment assembly, identifying top-1 CG structure through a CG scoring function, and rebuilding the all-atom structure from the top-1 CG one. Extensive examination on different types of RNA structures indicates that FebRNA gives consistently reliable predictions on RNA 3D structures including pseudoknots, 3-way junction, 4-way and 5-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available at website: https://github.com/Tan-group/FebRNA.

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“…Additionally, as suggested by Carrascoza et al. , the stereochemistry of RNA structures may be critical to distinguish native structures and decoy ones from structure prediction models ( 95 ). Thus, the involvement of stereochemistry in a statistical potential/scoring functions would possibly improve the performance for RNA 3D structure evaluation.…”

Section: Discussionmentioning

confidence: 99%

cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

Tan

Wang

et al. 2023

NAR Genomics and Bioinformatics

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Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP has similarly good performance for extensive types of test datasets and can have slightly better performance for the realistic dataset—RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom statistical potentials/scoring functions, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.

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Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions

Cited by 20 publications

References 54 publications

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models

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

cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

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