cRNAsp12 Web Server for the Prediction of Circular RNA Secondary Structures and Stabilities

Wang, Fengfei; Wei, Li; Li, Baiyi; Xie, Liangxu; Tong, Yunguang; Xu, Xiaojun

doi:10.3390/ijms24043822

Cited by 4 publications

(5 citation statements)

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“…There are four main steps to predict circular RNA 3D structure using IsRNAcirc model, including input preparation, end closure, structure prediction, and model refinement (Fig 2). Firstly, from the sequence information, cRNAsp12 tool [31] was used to predict the 2D structure of circular RNA molecules. And based on the sequence information and predicted 2D structure, RNAComposer [18] was utilized to generate three (if available) initial 3D structures for the corresponding linear counterpart.…”

Section: The Framework Of Isrnacirc For Circular Rna 3d Structure Pre...mentioning

confidence: 99%

“…IsRNAcirc requires the sequence information, 2D structure (in dot-bracket format), and an initial 3D structure (in PDB format) of the corresponding linear RNA as input to predict circular RNA 3D structures. From the sequence information, the 2D structure of circular RNAs was predicted by the cRNAsp12 tool [31] (http://xxulab.org.cn/crnasp12/) using the default set. As multiple 2D structure predictions were provided by cRNAsp12, we selected the structure with the lowest folding free energy for further analysis.…”

Section: Input and Outputmentioning

confidence: 99%

“…(A) Inputpreparation. From sequence information, cRNAsp12[31] was used to predict the 2D structure of circular RNAs. Based on the sequence and predicted 2D structure, RNAComposer[18] was used to generate initial 3D structures of the corresponding linear counterpart.…”

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confidence: 99%

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IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation

Jiang,

Xu,

Tong

et al. 2024

Preprint

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As an emerging class of RNA molecules, circular RNAs play pivotal roles in various biological processes, thereby determining their three-dimensional (3D) structure is crucial for a deep understanding of their biological significances. Similar to linear RNAs, the development of computational methods for circular RNA 3D structure prediction is challenging, especially considering the inherent flexibility and potentially long length of circular RNAs. Here, we introduce an extension of our previous IsRNA2 model, named IsRNAcirc, to enable circular RNA 3D structure predictions through coarse-grained molecular dynamics simulations. The workflow of IsRNAcirc consists of four main steps, including input preparation, end closure, structure prediction, and model refinement. Our results demonstrate that IsRNAcirc can provide reasonable 3D structure predictions for circular RNAs, which significantly reduce the locally irrational elements contained in the initial input. Moreover, for a validation test set comprising 34 circular RNAs, our IsRNAcirc can generate 3D models with better scores than the template-based 3dRNA method. These findings demonstrate that our IsRNAcirc method is a promising tool to explore the structural details along with intricate interactions of circular RNAs.Author SummaryKnowledge of the 3D structure of circular RNA molecules can help us better understand their roles in eukaryotic cells. However, experimental determination of circular RNA 3D structure remains challenging, with currently only one complete circular RNA 3D structure available in the Protein Data Bank. Thus, extending from their linear counterparts, computational methods that can predict circular RNA 3D structure are urgently needed. In particular, physics-basedde novo3D structure prediction methods are promising due to their powerful sampling capability in the conformational space, which avoid the problem of limited available templates in template-based approaches. We have developed IsRNAcirc, a new physics-based method that employs an accurate coarse-grained force field and replica-exchange molecular dynamics simulations to predict the 3D structure of circular RNAs. IsRNAcirc is shown to significantly reduce the number of locally irrational structures in the input trials, which are caused by the lack of suitable templates and the improper assembly of fragments. Furthermore, in the absence of experimental reference structures, the predicted 3D models were validated using the popular scoring functions, such as DFIRE-RNA and rsRNASP, demonstrating that IsRNAcirc outperforms the template-based 3dRNA method in generating low-energy conformations.

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Section: The Framework Of Isrnacirc For Circular Rna 3d Structure Pre...mentioning

confidence: 99%

Section: Input and Outputmentioning

confidence: 99%

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IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation

Jiang,

Xu,

Tong

et al. 2024

Preprint

Self Cite

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“…The selected sequences are examined for secondary structure by the Mfold web server 24 . The Mfold web server's default settings are used for this inquiry.…”

Section: Rna Secondary Structure Predictionmentioning

confidence: 99%

Untitled

2023

IJPSR

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A very tiny non-coding RNA molecule with a length of 20 to 24 nucleotides, called microRNAs or miRNAs. They control gene expression by targeting mRNAs with sequence-specificity. Depending on the degree of complementarity with the target mRNA sequences, this might result in translational repression or mRNA destruction. They are very crucial for controlling growth and development in plants. Researchers also agreed with the point that miRNAs are responsive to stress in plants. Growing in tropical and subtropical estuaries, mangrove is a very significant ecosystem both environmentally and commercially. But several natural processes, like sea level rise, salinity rise, and global warming, are disastrous for this flora, and some species are disappearing at a startling rate. Studying the function of these miRNAs in controlling mangrove homeostasis in that situation would be of utmost significance. In the most recent research, we discovered three miRNAs: ph-miR10630, ph-miR11471, and ph-miR10341 in Phoenix sp, that react to abiotic stress. This finding unmistakably indicates that miRNAs play a regulatory function in preserving cellular homeostasis. When compared to plants that are close to extinction, the experimental validation and molecular characterization of these miRNAs might provide important insights into their role in battling biotic and abiotic stress. This information would then be extremely helpful for planning the proper conservation of those plant species.INTRODUCTION: MicroRNAs (miRNAs) are a group of tiny, non-coding RNAs that play important regulatory roles in plants as well as in animals. They have persisted throughout evolution and are endogenous in nature 1 . miRNAs were first discovered in 1993 by Victor Ambros and colleagues, who noticed that the lin-4 gene, known to regulate the development efficiency of C. elegans, produced two small RNA molecules instead of a protein.

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“…An obvious shortcoming is the lack of focus on circular RNA; most prediction studies have concentrated on linear RNA, while circular RNA remains largely unexplored owing to insufficient experimental data that could serve as a knowledge base for analysis or predictive algorithms. However, there have been some attempts to address this gap, as seen in tools like CRIP [ 15 ], DeepciRGO [ 16 ], 3dRNA [ 17 ], cRNAsp12 [ 18 ], and other circRNA identification and prediction methodologies [ 19 , 20 ]. Additionally, many current algorithms exhibit suboptimal prediction accuracy in specific tissues.…”

Section: Introductionmentioning

confidence: 99%

BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs

Yuan,

Tang,

et al. 2024

Molecules

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m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications, a critical gap remains in the prediction of circRNA m6A modifications. Therefore, accurate identification and prediction of m6A sites are imperative for understanding RNA function and regulation. This study presents a novel hybrid model combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM) for precise m6A methylation site prediction in circular RNAs (circRNAs) based on data from HEK293 cells. This model exploits the synergy between CNN’s ability to extract intricate sequence features and BiLSTM’s strength in capturing long-range dependencies. Furthermore, the integrated attention mechanism empowers the model to pinpoint critical biological information for studying circRNA m6A methylation. Our model, exhibiting over 78% prediction accuracy on independent datasets, offers not only a valuable tool for scientific research but also a strong foundation for future biomedical applications. This work not only furthers our understanding of gene expression regulation but also opens new avenues for the exploration of circRNA methylation in biological research.

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cRNAsp12 Web Server for the Prediction of Circular RNA Secondary Structures and Stabilities

Cited by 4 publications

References 58 publications

IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation

IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation

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BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs

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