Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA Structure01:23

RNA Structure

29.8K
29.8K
RNA Structure01:19

RNA Structure

8.3K
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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
8.3K
RNA Structure01:23

RNA Structure

80.8K
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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
80.8K
¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

1.8K
The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.
1.8K
Nucleic Acid Structure01:25

Nucleic Acid Structure

10.3K
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.
DNA Structure
DNA...
10.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

397
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
397

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How Does Transcription-Associated Mutagenesis Shape tRNA Microevolution?

Genome biology and evolution·2026
Same author

Allelic Variation at tRNA Genes in Three Nematode Species Indicates Mutation Load Despite Strong Purifying Selection.

Genome biology and evolution·2026
Same author

Can geometric combinatorics improve RNA branching predictions?

BMC bioinformatics·2025
Same author

The R-loop grammar predicts R-loop formation under different topological constraints.

PLoS computational biology·2025
Same author

How much is Transcription-associated Mutagenesis Driving tRNA Microevolution?

bioRxiv : the preprint server for biology·2025
Same author

RNA profiling 2.0: Enhanced cluster analysis of structural ensembles.

ArXiv·2023

Related Experiment Video

Updated: Mar 29, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

5.4K

An efficient algorithm for exploring RNA branching conformations under the nearest-neighbor thermodynamic model.

Svetlana Poznanović1, Owen Cardwell2, Christine Heitsch3

  • 1School of Mathematical and Statistical Sciences, Clemson University, Clemson, 29634, SC, USA.

Algorithms for Molecular Biology : AMB
|March 28, 2026
PubMed
Summary

Reparameterizing RNA multiloop parameters improves structure prediction accuracy. A new algorithm efficiently explores alternative branching structures, making this optimization feasible for longer RNA sequences and large datasets.

Keywords:
MultiloopsNNTMRNA secondary structure

More Related Videos

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.5K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K

Related Experiment Videos

Last Updated: Mar 29, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

5.4K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.5K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • The Nearest-Neighbor Thermodynamic Model is standard for RNA secondary structure prediction.
  • Multiloop energy is modeled using a linear entropic penalty with three branching parameters.
  • Reparameterizing these parameters and exploring alternative branching conformations can improve predictions, but prior methods were computationally inefficient.

Purpose of the Study:

  • To develop a novel algorithm for efficient exploration of RNA multiloop parameter space.
  • To identify all distinct branching structures optimal under different branching parameters for a given RNA sequence.
  • To enable comprehensive evaluation of the structural landscape across parameter choices for improved RNA structure prediction.

Main Methods:

  • A novel algorithm partitions the parameter space to identify distinct branching structures.
  • The method uses minimal minimum free energy computations.
  • Efficiently computes the full parameter-space partition and associated optimal structures.

Main Results:

  • The algorithm efficiently computes the full parameter-space partition and optimal structures.
  • Application to the Archive II dataset shows substantial improvement potential over default predictions.
  • Optimal prediction accuracy is sensitive to auxiliary modeling decisions like treatment of lonely base pairs and dangling ends.

Conclusions:

  • Conventional multiloop parameters may limit RNA structure prediction accuracy.
  • Exploring alternative parameterizations is tractable and worthwhile for improving predictions.
  • The efficient partitioning algorithm makes this exploration feasible for longer sequences and larger datasets.