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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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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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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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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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DRfold2 is a deep learning-based tool that enables efficient and accurate RNA structure prediction.

Yang Li1, Chenjie Feng2, Xi Zhang3

  • 1Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.

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This summary is machine-generated.

DRfold2, a new deep learning tool, accurately predicts RNA structures from sequence alone. It enhances RNA-targeted therapeutics by improving structure prediction accuracy significantly.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Accurate RNA structure prediction is vital for understanding biological roles and developing RNA-based therapies.
  • Predicting RNA structure solely from sequence remains a significant computational challenge.

Purpose of the Study:

  • To introduce DRfold2, a deep learning framework for end-to-end RNA structure prediction from single sequences.
  • To evaluate DRfold2's performance against state-of-the-art methods in predicting global topology and secondary structures.

Main Methods:

  • DRfold2 integrates a pre-trained RNA Composite Language Model (RCLM) with a denoising structure module.
  • The framework utilizes deep learning for end-to-end prediction and deep learning-guided post-optimization.
  • Performance was benchmarked across diverse species using multiple test sets.

Main Results:

  • DRfold2 demonstrated superior performance in both global topology and secondary structure prediction compared to existing methods.
  • The RNA Composite Language Model captured co-evolutionary patterns, and the denoising module improved contact prediction precision by over 100%.
  • DRfold2 showed significant accuracy improvements when integrated with AlphaFold3.

Conclusions:

  • DRfold2 advances ab initio RNA structure prediction by combining composite language modeling and denoising-based end-to-end learning.
  • The framework offers a novel approach for accurate RNA structure prediction from sequence.
  • DRfold2 holds promise for accelerating the development of RNA-targeted therapeutics.