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Related Concept Videos

RNA Structure01:19

RNA Structure

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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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RNA-seq03:21

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.

Deyin Wang1, Yangwei Jiang1,2, Linli He2

  • 1Institute of Quantitative Biology, School of Physics, and College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China.

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|April 2, 2026
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Summary

RNA 3D structure prediction accuracy depends on input 2D structure quality. Models that modify base pairs during 3D modeling perform better, especially when input 2D structures have fewer false positives.

Keywords:
RNA 2D structure predictionRNA 3D structure predictionbenchmarkingcrosstalkmodel improvement

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Computational methods predict RNA 3D structures.
  • RNA secondary (2D) structure aids 3D prediction accuracy and efficiency.
  • The impact of 2D structure accuracy on 3D prediction is understudied.

Purpose of the Study:

  • Benchmark RNA 3D structure prediction models.
  • Investigate the effect of 2D structure accuracy on 3D prediction.
  • Explore modification of base-pairing interactions during 3D modeling.

Main Methods:

  • Benchmarking six RNA 3D structure prediction models.
  • Utilizing datasets with 2D structures of varying accuracies.
  • Analyzing the relationship between 2D input accuracy and 3D model performance.

Main Results:

  • Significant crosstalk exists between RNA 2D and 3D structure predictions.
  • 3D prediction accuracy depends on the model's ability to adjust input base pairs.
  • Models are more sensitive to false positive base pairs in 2D structures.

Conclusions:

  • RNA 3D structure prediction is influenced by 2D structure accuracy and model flexibility.
  • Improving 2D structure prediction, particularly reducing false positives, can enhance 3D modeling.
  • Understanding base-pair modification is key to advancing RNA structure prediction.