GHOST-NOT and GHOST-YES: Two programs for generating high-speed biosensors with randomized oligonucleotide binding...

Area of Science:

• Molecular Biology
• Biotechnology
• Bioinformatics

Background:

• Allosteric hammerhead ribozymes offer precise control over gene expression.
• Existing in vivo selection methods for ribozyme engineering are complex and time-consuming.
• Computational approaches can accelerate the design of functional ribozymes.

Purpose of the Study:

• To present computational tools for designing high-speed allosteric ribozymes.
• To enable the creation of ribozymes that sense specific oligonucleotide sequences.
• To develop ribozymes for potential gene therapy applications, including anticancer treatments.

Main Methods:

• In silico selection using RNA secondary structure computation and partition function analysis.
• Utilizing random search algorithms for ribozyme design.
• Development of two programs: GHOST-NOT and GHOST-YES for computational design.

Main Results:

• Designed allosteric hammerhead ribozymes exhibit high self-cleavage rates (~1.8/min).
• Ribozymes demonstrate high selectivity in sensing effector sequences, distinguishing perfect matches from sequences with mismatches.
• The computational approach proved accurate in designing allosteric hammerhead ribozymes.

Conclusions:

• In silico design is an effective strategy for creating high-speed, selective allosteric ribozymes.
• The GHOST programs facilitate the development of ribozymes for gene expression control.
• Engineered ribozymes hold promise for advanced gene therapies and biotechnological applications.