RaGene is merging computational biology with AI to tackle biological complexity and unlock the true potential of codon substitution design.
Codon optimization (C.O) has long been a standard method for the enhancement of protein yield, Since a given protein can be encoded by a variety of DNA sequences, there is a drive to generate or identify the best expressing sequence.
The rational approach of C.O disturbs the natural complexity of sequences, as a result it is limited in enhancement potential, and prone to issues resulting from this disturbance such as undesired protein conformation and stability alterations
RaGene’s AI-based models go far beyond the rational limits by deeply deciphering the biological complexity and writing new optimization patterns inspired by biology.
Guided by deep learning from mother nature, our algorithms effectively identify the golden balance between the multiple optimization parameters, which lead to the maximum protein expression rates without affecting the encrypted folding patterns
In this project we worked with a pharma CRO which had issues expressing two different complex antibodies. We used RaGene to generate new sequences. Results are compared against a control which was optimized by a top industry standard tool
In this project we decided to challenge RaGene to enhance the yield of two of the most highly-optimized and high expressing fluorescent proteins, EGFP and TagBFP
Fluorescence increase
Fluorescence increase
