Computational tool to predict RNA binding protein targets for gene therapy and agricultural bioengineering

This technology is a computational algorithm for code prediction and target site matching of RNA-binding pentetricopeptide repeat (PPR) proteins called PPRDecoder.

Unmet Need: Accurate computational prediction tool of RNA-binding protein specificity for gene regulation

The current gold standard for predicting target sites of RNA-binding proteins, like pentetricopeptide repeat (PPR) proteins, relies on empirical evidence which is time-consuming and limited in scope. This approach lacks a comprehensive understanding of the RNA recognition code and often fails to capture the specificity of PPR proteins. This technology is a computational algorithm that infers the PPR code and accurately predicts target sites on a genome-wide scale without relying on experimental data. This technology enables a more efficient and unbiased approach to understanding gene regulation and facilitates the discovery of novel transcriptional regulators.

The Technology: Algorithm to match PPR proteins with their targets in a genome-wide manner

PPRDecoder is a computational algorithm designed to predict the target sites of RNA-binding proteins known as pentetricopeptide repeat (PPR) proteins. By analyzing the patterns and interactions within PPR proteins and RNA sequences, the algorithm infers a quantitative and predictive “PPR code” statistically. This allows for the identification of specific RNA targets for PPR proteins without the need for extensive empirical testing. The technology’s value lies in its genome-wide and unbiased approach, saving time and resources by eliminating the need for experimental validation. PPRDecoder has the potential to uncover novel proteins involved in transcriptional regulation, making it applicable in agricultural bioengineering and biotechnology for gene therapy.

This technology has been validated with hornwort Anthoceros agrestis plant samples.

Applications:

• Research tool for RNA regulation and gene expression
• Discovery of gene regulators for agricultural bioengineering and precision medicine applications
• Gene expression modulation
• Enhancement of genetic disorder treatments

Advantages:

• Capable of prediction novel pentetricopeptide repeat (PPR)-RNA pairs
• Unbiased algorithm that does not rely on experimental data
• Computational platform for both PPR matching and PPR code prediction
• High accuracy predictions

Lead Inventor:

Chaolin Zhang, Ph.D.

Patent Information:

Patent Pending

Tech Ventures Reference:

• IR CU23342

• Licensing Contact: Joan Martínez