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Related Concept Videos

RNA Structure01:23

RNA Structure

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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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Related Experiment Video

Updated: Aug 26, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA secondary structure packages evaluated and improved by high-throughput experiments.

Hannah K Wayment-Steele1,2, Wipapat Kladwang2,3, Alexandra I Strom3,4

  • 1Department of Chemistry, Stanford University, Stanford, CA, USA.

Nature Methods
|October 3, 2022
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Summary
This summary is machine-generated.

EternaBench dataset reveals CONTRAfold and RNAsoft RNA structure modeling packages outperform others. A new model, EternaFold, trained on this data shows improved accuracy for RNA ensemble prediction tasks.

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Area of Science:

  • Computational biology
  • Biophysics
  • Bioinformatics

Background:

  • Computer-aided RNA design is popular, but accuracy in ensemble-property-sensitive tasks is understudied.
  • Existing RNA structure modeling packages vary in performance, particularly for ensemble-based predictions.

Purpose of the Study:

  • To evaluate the accuracy of RNA structure modeling packages for ensemble-oriented prediction tasks.
  • To introduce and assess a novel multitask model, EternaFold, for improved RNA structure inference.

Main Methods:

  • Utilized the EternaBench dataset, comprising over 20,000 synthetic RNA constructs from the Eterna platform.
  • Compared the performance of CONTRAfold, RNAsoft, and other widely used packages.
  • Trained a multitask model (EternaFold) using EternaBench data for ensemble-based prediction.

Main Results:

  • CONTRAfold and RNAsoft, utilizing statistical learning parameters, showed higher accuracy than packages relying on thermodynamic experimental parameters.
  • EternaFold demonstrated superior performance in ensemble-based prediction tasks.
  • EternaFold's improved accuracy generalized to various external datasets, including messenger RNAs and viral genomes.

Conclusions:

  • The EternaBench dataset is a valuable resource for evaluating RNA structure modeling packages.
  • Statistical learning-based parameterization enhances RNA structure prediction accuracy.
  • EternaFold offers a promising advancement for accurate RNA ensemble structure prediction across diverse biological contexts.