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Related Experiment Video

Updated: May 6, 2026

In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

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Visualizing the ai5γ group IIB intron.

Srinivas Somarowthu¹, Michal Legiewicz, Kevin S Keating

¹Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA, Department of Chemistry, Yale University, New Haven, CT 06511, USA and Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.

Nucleic Acids Research

|November 9, 2013

Summary

This summary is machine-generated.

Computational modeling of large noncoding RNA structures, like the yeast ai5γ group IIB intron, is crucial for understanding cellular metabolism. This study developed new methods to model and validate these complex RNA molecules, revealing insights into splicing regulation.

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Area of Science:

Structural biology
Computational biology
Molecular genetics

Background:

Large noncoding RNA molecules play significant roles in cellular metabolism.
Crystallographic methods are limited for visualizing large RNA 3D structures, necessitating computational approaches.

Purpose of the Study:

To model the 3D molecular structure of the yeast ai5γ group IIB intron.
To develop and validate computational tools for modeling large functional RNA molecules.

Main Methods:

Adapted homology and de novo modeling strategies.
Developed a novel computational tool for RNA refinement.
Validated the model using structure-guided mutagenesis and RNA structure probing.

Main Results:

Generated a validated 3D structural model of the ai5γ group IIB intron.
Gained insights into splicing mechanisms, including branch-site positioning and subdomain roles in target specificity.
Demonstrated the feasibility of modeling large functional RNA molecules.

Conclusions:

Computational modeling is a viable approach for studying large RNA structures.
The developed model provides significant insights into RNA splicing and regulation.
This work advances the understanding of noncoding RNA function in cellular processes.