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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Protein-RNA interaction prediction with deep learning: structure matters.

Junkang Wei1, Siyuan Chen2, Licheng Zong1

  • 1Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), 999077, Hong Kong SAR, China.

Briefings in Bioinformatics
|December 20, 2021
PubMed
Summary
This summary is machine-generated.

This review surveys RNA-binding protein-RNA interactions, covering prediction methods and datasets. It highlights the impact of AlphaFold, anticipating future advancements in computational approaches for these vital cellular interactions.

Keywords:
RNA structuredeep learningprotein structureprotein–RNA interaction

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Protein-RNA interactions are crucial for cellular functions.
  • Existing computational methods often lack protein structure data, relying primarily on sequence information.
  • Recent advancements like AlphaFold are poised to significantly enhance protein structure prediction.

Purpose of the Study:

  • To provide a comprehensive review of RNA-binding protein-RNA interaction prediction.
  • To survey common datasets, features, and models used in the field.
  • To discuss future challenges and opportunities in the post-AlphaFold era.

Main Methods:

  • Literature review of existing computational and experimental techniques.
  • Analysis of datasets, features, and predictive models for binding site and preference prediction.
  • Discussion of the impact of structural biology advancements, particularly AlphaFold.

Main Results:

  • Identified limitations in current databases and methods, especially regarding structural data.
  • Cataloged prevalent approaches for predicting RNA-binding protein-RNA interactions.
  • Highlighted the transformative potential of AlphaFold in advancing the field.

Conclusions:

  • The field of RNA-binding protein-RNA interaction prediction is rapidly evolving.
  • AlphaFold integration promises more accurate and structure-aware prediction models.
  • Future research should leverage structural data for enhanced understanding and prediction.