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Updated: Jun 12, 2025

Substrate Generation for Endonucleases of CRISPR/Cas Systems

Substrate Generation for Endonucleases of CRISPR/Cas Systems

Published on: September 8, 2012

Generating Artificial Ribozymes Using Sparse Coevolutionary Models.

Francesco Calvanese^1,2, Martin Weigt¹, Philippe Nghe³

¹Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative - LCQB Paris, Paris, France.

Methods in Molecular Biology (Clifton, N.J.)

|September 23, 2024

Summary

This summary is machine-generated.

Generative models explore vast RNA sequence space beyond natural evolution. Direct coupling analysis (DCA) creates novel twister ribozymes and predicts mutation effects.

Keywords:

Artificial RNA sequences DCA Direct coupling analysis Generative model Mutational effects

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Area of Science:

Computational Biology
Molecular Biology
Bioinformatics

Background:

RNA sequence datasets are limited compared to the vast potential RNA sequence space.
Natural evolution has explored only a fraction of possible RNA sequences.
Generative models offer a powerful approach to explore uncharted sequence variability.

Purpose of the Study:

To apply a generative model based on direct coupling analysis (DCA) to the twister ribozyme RNA family.
To generate artificial twister ribozymes.
To design functional mutations and predict mutational effects.

Main Methods:

Utilized a generative model grounded in direct coupling analysis (DCA).
Applied the model to the twister ribozyme RNA family.
Focused on generating novel sequences, designing mutations, and predicting effects.

Main Results:

Successfully generated artificial twister ribozymes.
Identified potentially functional mutations for a natural wild-type sequence.
Demonstrated the model's capability in predicting mutational effects.

Conclusions:

Generative models, particularly DCA-based ones, are effective for exploring RNA sequence space.
This approach expands the known repertoire of twister ribozymes.
Facilitates the design of novel RNA molecules with potential functional properties.