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Nucleic Acid Structure01:25

Nucleic Acid Structure

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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 Structure01:19

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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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Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Multiple sequence alignment-based RNA language model and its application to structural inference.

Yikun Zhang1,2, Mei Lang3, Jiuhong Jiang3

  • 1School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.

Nucleic Acids Research
|November 9, 2023
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Summary
This summary is machine-generated.

We developed RNA-MSM, an unsupervised RNA language model using multiple sequence alignments, which captures structural information and outperforms existing methods for predicting RNA structure and function.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Interpreting RNA sequences is challenging due to their lower information content compared to proteins.
  • Existing RNA language models, like BERT-based ones, struggle to capture evolutionary information from less conserved RNA sequences.

Purpose of the Study:

  • To develop an unsupervised RNA language model that effectively utilizes homologous sequences.
  • To demonstrate that the model can capture and represent structural information within RNA sequences.

Main Methods:

  • Developed RNA-MSM, an unsupervised multiple sequence alignment-based RNA language model.
  • Utilized homologous sequences from the RNAcmap pipeline for training.
  • Evaluated the model's ability to predict 2D base pairing probabilities and 1D solvent accessibilities.

Main Results:

  • RNA-MSM's attention maps and embeddings contain structural information, mapping accurately to base pairing and solvent accessibility.
  • Fine-tuned RNA-MSM significantly improved performance on downstream tasks compared to state-of-the-art methods.
  • RNA-FM, a BERT-based model, performed worse than simpler methods for structure prediction.

Conclusions:

  • RNA-MSM offers a powerful new approach for RNA sequence analysis and structure prediction.
  • The pre-trained RNA-MSM model can be adapted for various RNA structure and function-related tasks.
  • This method addresses limitations of existing RNA language models in capturing evolutionary and structural information.